SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Questions and Answers

AWS Step Functions is a fully managed service that makes it easy to build applications by coordinating the components of distributed applications and microservices using visual workflows. With Step Functions, you can combine multiple AWS Lambda functions into responsive serverless applications and orchestrate data and services that run on Amazon EC2 instances, containers, or on-premises servers. Step Functions also allows for manual approvals as part of the workflow. This solution meets all the requirements with the least operational overhead.

https://aws.amazon.com/step-functions/#:~:text=AWS%20Step%20Functions%20is%20a,machine%20learning%20(ML)%20pipelines.

This solution is the most scalable and efficient way to handle large volumes of survey data while automating sentiment analysis:

API Gateway and SQS: The survey results are sent to API Gateway, which forwards the data to an SQS queue. SQS can handle large volumes of messages and ensures that messages are not lost.

AWS Lambda: Lambda is triggered by polling the SQS queue, where it processes the survey data.

Amazon Comprehend: Comprehend is used for sentiment analysis, providing insights into customer satisfaction.

DynamoDB with TTL: Results are stored in DynamoDB with aTime to Live (TTL)attribute set to expire after 365 days, automatically removing old data and reducing storage costs.

Option B (EC2 API): Running an API on EC2 requires more maintenance and scalability management compared to API Gateway.

Option C (S3 and Rekognition): Amazon Rekognition is for image and video analysis, not sentiment analysis.

Option D (Amazon Lex): Amazon Lex is used for building conversational interfaces, not sentiment analysis.

AWS References:

Amazon Comprehend for Sentiment Analysis

Amazon SQS

DynamoDB TTL

Understanding the Requirement: Senior leadership wants a custom dashboard displaying NAT gateway costs daily, starting from the beginning of the current month.

Analysis of Options:

QuickSight with DataSync: While QuickSight is suitable for dashboards, DataSync is not designed for querying and analyzing data reports.

QuickSight with Athena: QuickSight can visualize data queried by Athena, which is designed to analyze data directly from S3.

CloudWatch with DataSync: CloudWatch is primarily for monitoring metrics, not for creating detailed cost analysis dashboards.

CloudWatch with Athena: Similarly, using CloudWatch with Athena does not align well with the requirement for a visual dashboard.

Best Solution for Visualization and Querying:

Amazon QuickSight with Athena: This combination allows for powerful data visualization and querying capabilities. QuickSight can create dynamic dashboards, while Athena efficiently queries the cost and usage report data stored in S3.

Understanding the Requirement: The company anticipates a spike in traffic during a holiday and wants to ensure the Auto Scaling group can handle the increased load without impacting performance.

Analysis of Options:

CloudWatch Alarm: This reacts to spikes based on metrics like CPU utilization but does not proactively scale before the anticipated demand.

Recurring Scheduled Action: This allows the Auto Scaling group to scale up based on a known schedule, ensuring additional capacity is available before the expected spike.

Increase Min/Max Instances: This could result in unnecessary costs by maintaining higher capacity even when not needed.

SNS Notification: Alerts on scaling events but does not proactively manage scaling to prevent performance issues.

Best Solution for Proactive Scaling:

Create a recurring scheduled action: This approach ensures that the Auto Scaling group scales up before the peak demand, providing the necessary capacity proactively without manual intervention.

Send the outbound connection from EC2 to Network Firewall. In Network Firewall, create stateful outbound rules to allow certain domains for software patch download and deny all otherdomains.https://docs.aws.amazon.com/network-firewall/latest/developerguide/suricata-examples.html#suricata-example-domain-filtering

Understanding the Requirement: The company wants to restrict each application to its specific prefix in an S3 bucket and have granular control over the objects under each prefix.

Analysis of Options:

Dedicated S3 Access Points: Provides a scalable and flexible way to manage access to S3 buckets, allowing specific policies to be attached to each access point, thereby controlling access at the prefix level.

S3 Batch Operations: Suitable for large-scale changes but involves more operational overhead and does not dynamically control future access.

Replication to new S3 buckets: Involves unnecessary duplication of data and increased storage costs, and operational overhead for managing multiple buckets.

Combination of replication and access points: Adds unnecessary complexity and overhead compared to using access points directly.

Best Solution:

Dedicated S3 Access Points: This provides the least operational overhead while meeting the requirements for prefix-level access control and granular management.

Understanding the Requirement: The company wants to migrate an application to AWS, increase its availability, and use AWS WAF in the architecture.

Analysis of Options:

Auto Scaling group with ALB and WAF: This option provides high availability by distributing instances across multiple Availability Zones. The ALB ensures even traffic distribution, and AWS WAF provides security at the application layer.

Cluster placement group with ALB and WAF: Cluster placement groups are for low-latency networking within a single AZ, which does not provide the high availability across AZs.

Two EC2 instances with ALB and WAF: This setup provides some availability but does not scale automatically, missing the benefits of an Auto Scaling group.

Auto Scaling group with WAF directly: AWS WAF cannot be directly connected to an Auto Scaling group; it needs to be attached to an ALB, CloudFront distribution, or API Gateway.

Best Solution:

Auto Scaling group with ALB and WAF: This configuration ensures high availability, scalability, and security, meeting all the requirements effectively.

Understanding the Requirement: The job is stateful, cannot tolerate interruptions, and needs to run reliably for at least one hour each week.

Analysis of Options:

AWS Fargate with Amazon ECS and EventBridge: This option provides a serverless compute engine for containers that can run stateful tasks reliably. Using EventBridge Scheduler, the job can be triggered automatically at the specified time without manual intervention.

AWS Lambda with EventBridge: Lambda functions are not suitable for long-running stateful jobs since they have a maximum execution time of 15 minutes.

EC2 Spot Instances: Spot Instances can be interrupted, making them unsuitable for a stateful job that cannot tolerate interruptions.

AWS DataSync: This service is primarily for moving large amounts of data and is not designed to run stateful analysis jobs.

Best Option for Reliable, Scheduled Execution:

The Fargate task on ECS with EventBridge Scheduler meets all requirements, providing the necessary reliability and scheduling capabilities without interruption risks.

Understanding the Requirement: The company needs to map its IT infrastructure to identify and enforce security policies, with the ability to quickly query and identify security risks.

Analysis of Options:

Amazon RDS: While suitable for relational data, it is not optimized for handling complex relationships and querying those relationships, which is essential for an IT infrastructure map.

Amazon Neptune: A graph database service designed for handling highly connected data. It uses SPARQL to query graph data efficiently, making it ideal for mapping IT infrastructure and identifying relationships that pose security risks.

Amazon Redshift: A data warehouse solution optimized for complex queries on large datasets but not specifically for graph data.

Amazon DynamoDB: A NoSQL database that uses PartiQL for querying, but it is not optimized for complex relationships in graph data.

Best Option for Mapping and Querying IT Infrastructure:

Amazon Neptune provides the most suitable solution with the least operational overhead. It is purpose-built for graph data and enables efficient querying of complex relationships to identify security risks.

Understanding the Requirement: The company has petabytes of data in S3 Standard across multiple buckets with varying access frequencies. They do not know the access patterns and need a cost-optimized storage solution with minimal operational effort.

Analysis of Options:

S3 Intelligent-Tiering: This storage class automatically moves data between two access tiers (frequent and infrequent) based on changing access patterns. It incurs a small monitoring and automation charge but eliminates the need to manually move data between storage classes.

S3 Storage Class Analysis Tool: While useful for determining access patterns, this tool requires manual intervention to move objects to the appropriate storage class, which increases operational overhead.

S3 Glacier Instant Retrieval: This storage class is designed for data that is rarely accessed but requires instant retrieval when needed. It may not be suitable for data with unknown and varying access patterns.

S3 One Zone-IA: This is a lower-cost option for infrequently accessed data stored in a single availability zone. It does not provide the same level of durability and availability as other options and requires knowledge of access patterns.

Best Option for Operational Efficiency:

S3 Intelligent-Tiering provides the best balance of cost savings and operational efficiency. It dynamically adjusts to access patterns without manual intervention, ensuring the company is only paying for what they need without the risk of incurring high costs for infrequent access data.

Understanding the Requirement: The company wants to maximize cost savings for their application over the next three years, with the flexibility to change the instance family and sizes within the next six months based on application popularity and usage.

Analysis of Options:

Compute Savings Plan: This plan offers the most flexibility, allowing the company to change instance families, sizes, and regions. It applies to EC2, AWS Fargate, and AWS Lambda, offering significant cost savings with this flexibility.

EC2 Instance Savings Plan: This plan is less flexible than the Compute Savings Plan, as it only applies to EC2 instances and allows changes within a specific instance family.

Zonal Reserved Instances: These provide a discount on EC2 instances but are tied to a specific availability zone and instance type, offering the least flexibility.

Standard Reserved Instances: These offer discounts on EC2 instances but with more restrictions compared to Savings Plans, particularly when changing instance types and families.

Best Option for Flexibility and Savings:

The Compute Savings Plan is the most cost-effective solution because it allows the company to maintain flexibility while still achieving significant cost savings. This is critical for adapting to changing application demands without being locked into specific instance types or families.

Understanding the Requirement: The company needs to control the creation, rotation, and disabling of encryption keys for data stored in S3 with minimal effort.

Analysis of Options:

SSE-S3: Provides server-side encryption using S3 managed keys but does not offer full control over key management.

Customer managed key with AWS KMS (SSE-KMS): Allows the company to fully control key creation, rotation, and disabling, providing a high level of security and compliance.

AWS managed key with AWS KMS (SSE-KMS): While it provides some control, it does not offer the same level of granularity as customer-managed keys.

EC2 instance encryption and re-upload: This approach is operationally intensive and does not leverage AWS managed services for efficient key management.

Best Solution:

Customer managed key with AWS KMS (SSE-KMS): This solution meets the requirement for full control over encryption keys with minimal operational overhead, leveraging AWS managed services for secure key management.

Understanding the Requirement: The company needs to mitigate DDoS attacks on its website, which uses AWS Global Accelerator to route traffic to an Application Load Balancer (ALB).

Analysis of Options:

AWS WAF on Global Accelerator: Allows for centralized protection and can block traffic based on rate-based rules, effectively mitigating DDoS attacks with minimal implementation effort.

Lambda Function and VPC Network ACL: Requires custom implementation and ongoing management, increasing complexity and effort.

AWS WAF on ALB: Provides protection but involves additional configuration and management at the ALB level.

CloudFront Distribution in front of Global Accelerator: Adds unnecessary complexity and changes the current traffic flow setup.

Best Solution:

AWS WAF on Global Accelerator: This provides the required protection with the least implementation effort, ensuring effective DDoS mitigation and maintaining the existing architecture.

Understanding the Requirement: The company wants to cost-optimize their workload that uses EC2, Lambda, Fargate, and SageMaker, covering the most services with the fewest savings plans.

Analysis of Options:

EC2 Instance Savings Plan: Limited to EC2 and SageMaker, missing coverage for Lambda and Fargate.

Compute Savings Plan: Provides the most flexibility, covering a broad range of compute services, including EC2, Lambda, Fargate, and SageMaker.

SageMaker Savings Plan: Specifically for SageMaker, missing coverage for EC2, Lambda, and Fargate.

Combination of plans: The Compute Savings Plan is versatile and can be combined to cover different services efficiently.

Best Solution:

Compute Savings Plan for EC2, Lambda, and SageMaker: Covers the primary compute services efficiently.

Compute Savings Plan for Lambda, Fargate, and EC2: Covers the remaining services, ensuring broad coverage with minimal plans.

Deploy Amazon API Gateway as an HTTPS endpoint and AWS Lambda to process and save the messages to an Amazon DynamoDB table. This option provides a highly available and scalable solution that can easily handle large amounts of data. It also integrates with other AWS services, making it easier to analyze and visualize the data for the security team.

Understanding the Requirement: The company needs to store a copy of all new photos in the us-east-1 Region from an S3 bucket in the us-west-1 Region.

Analysis of Options:

Cross-Region Replication: Automatically replicates objects across regions with minimal operational effort once configured.

CORS Configuration: Used for allowing resources on a web page to be requested from another domain, not for replication.

S3 Lifecycle Rule: Manages the transition of objects between storage classes within the same bucket, not for cross-region replication.

S3 Event Notifications with Lambda: Requires additional configuration and management compared to Cross-Region Replication.

Best Solution:

S3 Cross-Region Replication: This solution provides an automated and efficient way to replicate objects to another region, meeting the requirement with the least operational effort.

AWS Outposts is a fully managed service that delivers AWS infrastructure and services to virtually any on-premises or edge location for a consistent hybrid experience. AWS Outposts supports Amazon EKS, which is a managed service that makes it easy to run Kubernetes on AWS and on-premises. By installing an AWS Outposts rack in the company’s data center, the company can run containers in a Kubernetes environment using Amazon EKS and other AWS managed services, while keeping the data locally in the company’s data center and meeting the compliance requirements. AWS Outposts also provides a seamless connection to the local AWS Region for access to a broad range of AWS services.

Option A is not a valid solution because AWS Local Zones are not deployed in the company’s data center, but in large metropolitan areas closer to end users. AWS Local Zones are owned, managed, and operated by AWS, and they provide low-latency access to the public internet and the local AWS Region. Option B is not a valid solution because AWS Snowmobile is a service that transports exabytes of data to AWS using a 45-foot long ruggedized shipping container pulled by a semi-trailer truck. AWS Snowmobile is not designed for running containers or AWS managed services on-premises, but for large-scale data migration. Option D is not a valid solution because AWS Snowball Edge Storage Optimized is a device that provides 80 TB of HDD or 210 TB of NVMe storage capacity for data transfer and edge computing. AWS Snowball Edge Storage Optimized does not support Amazon EKS or other AWS managed services, and it is not suitable for running containers in a Kubernetes environment.

https://aws.amazon.com/solutions/implementations/aws-streaming-data-solution-for-amazon-kinesis/

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

The simplest and most effective way to ensure that all data that is written to the EBS volumes is encrypted at rest is to create the EBS volumes as encrypted volumes. You can do this by selecting the encryption option when you create a new EBS volume, or by copying an existing unencrypted volume to a new encrypted volume. You can also specify the AWS KMS key that you want to use for encryption, or use the default AWS-managed key. When you attach the encrypted EBS volumes to the EC2 instances, the data will be automatically encrypted and decrypted by the EC2 host. This solution does not require any additional IAM roles, tags, or policies.

Understanding the Requirement: The company needs to keep daily EBS snapshots for 1 month and retain monthly snapshots for 7 years due to new regulations.

Analysis of Options:

S3 Glacier Deep Archive: Moving snapshots to S3 Glacier Deep Archive involves additional complexity and might not be the most straightforward approach for EBS snapshots.

EBS Snapshots Archive: This is a cost-effective solution designed specifically for long-term storage of EBS snapshots.

Standard Tier for 7 Years: Keeping snapshots in the standard tier for 7 years is more expensive and does not optimize costs.

EBS Direct APIs to S3: This involves additional operational overhead and is not the most cost-effective approach compared to using EBS Snapshots Archive.

Best Solution:

EBS Snapshots Archive: Adding a policy to move monthly snapshots to the EBS Snapshots Archive for long-term retention is the most cost-effective and administratively simple solution.

Understanding the Requirement: The company needs to copy files generated by an on-premises application to AWS without modifying the application code. The files are stored on an SMB file share and require a low-latency connection to the application servers.

Analysis of Options:

Amazon Elastic File System (EFS): EFS is designed for Linux-based workloads and does not natively support SMB file shares.

Amazon FSx for Windows File Server: FSx supports SMB file shares but would require changes to the application or additional infrastructure to connect on-premises systems.

AWS Snowball: Suitable for large data transfers but not for continuous, low-latency file copying.

AWS Storage Gateway: Provides a hybrid cloud storage solution, supporting SMB file shares and enabling seamless copying of files to AWS without requiring changes to the application.

Best Solution:

AWS Storage Gateway: This service meets the requirement for a low-latency, seamless file transfer solution from on-premises to AWS without modifying the application code.

The most cost-effective solution for the online video game company is to configure a Network Load Balancer with the required protocol and ports for the internet traffic and specify the EC2 instances as the targets. This solution will enable the company to handle millions of UDP requests per second with ultra-low latency and high performance.

A Network Load Balancer is a type of Elastic Load Balancing that operates at the connection level (Layer 4) and routes traffic to targets (EC2 instances, microservices, or containers) within Amazon VPC based on IP protocol data. A Network Load Balancer is ideal for load balancing of both TCP and UDP traffic, as it is capable of handling millions of requests per second while maintaining high throughput at ultra-low latency. A Network Load Balancer also preserves the source IP address of the clients to the back-end applications, which can be useful for logging or security purposes1.

Requirement Analysis: The company needs highly available, scalable storage for a document management application without modifying the application during migration.

EFS Overview: Amazon EFS provides scalable file storage that can be mounted concurrently on multiple EC2 instances across different Availability Zones.

EFS Standard-IA: Using the Standard-IA storage class helps reduce costs for infrequently accessed data while maintaining high availability and scalability.

Implementation:

Create an EFS file system.

Configure mount targets in multiple Availability Zones to ensure high availability.

Mount the EFS file system on EC2 instances in the Auto Scaling group.

Conclusion: This solution meets the high availability, scalability, and cost-effectiveness requirements without needing application modifications.

References

Amazon EFS:Amazon EFS Documentation

EFS Storage Classes:Amazon EFS Storage Classes

To use AWS Budgets to create and manage budgets for different AWS accounts, the company needs to do the following steps:

Use AWS Budgets to create a budget for each AWS account that needs a different budget amount. The budget can be based on cost or usage metrics, and can have different time periods, filters, and thresholds. The company can set the budget amount under the Billing dashboards of the required AWS accounts1.

Create an IAM role for AWS Budgets to run budget actions with the required permissions. A budget action is a response that AWS Budgets initiates when a budget exceeds a specified threshold. The IAM role allows AWS Budgets to perform actions on behalf of the company, such as applying an IAM policy or a service control policy (SCP) to restrict the provisioning of additional resources2.

Add an alert to notify the company when each account meets its budget threshold. The alert can be sent via email or Amazon SNS. The company can also add a budget action that selects the IAM role created and the appropriate SCP to prevent provisioning of additional resources. An SCP is a type of policy that can be applied to an AWS account or an organizational unit (OU) within AWS Organizations. An SCP can limit the actions that users and roles can perform in the account or OU3.

This solution usesCloudFrontto serve the website securely over HTTPS usingAWS Certificate Manager (ACM)for SSL certificates.Origin Access Control (OAC)ensures that only CloudFront can access the S3 bucket directly.AWS WAFwith an IP set rule restricts access to the website, allowing only the on-premises IP address.Route 53is used to create an alias record pointing to the CloudFront distribution. This setup ensures secure, private access to the website with low administrative overhead.

Option A and B: S3 bucket policies and access points do not provide HTTPS support, nor do they offer the same level of security as CloudFront with WAF.

Option D: Signed URLs are more suitable for temporary, expiring access rather than a permanent solution for on-premises employees.

AWS References:

Amazon CloudFront with Origin Access Control

Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.

Analysis of Options:

IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.

AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.

IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.

Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.

Best Solution:

Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts, effectively preventing the launch of large EC2 instances.

Understanding the Requirement: The company needs to enable communication between VPCs across multiple AWS Regions with minimal administrative effort.

Analysis of Options:

VPC Peering: Managing multiple VPC peering connections across regions is complex and difficult to scale, leading to significant administrative overhead.

AWS Direct Connect Gateways: Primarily used for creating private connections between AWS and on-premises environments, not for inter-VPC communication across regions.

AWS Transit Gateway: Simplifies VPC interconnections within a region and supports Transit Gateway peering for cross-region connectivity, reducing administrative complexity.

AWS PrivateLink: Used for accessing AWS services and third-party services over a private connection, not for inter-VPC communication.

Best Solution:

AWS Transit Gateway with Transit Gateway Peering: This option provides a scalable and efficient solution for managing VPC communications both within a single region and across multiple regions with minimal administrative overhead.

 S3 Lifecycle is a feature that allows you to automate the management of your S3 objects based on predefined rules. You can use S3 Lifecycle to delete expired object versions and retain the two most recent versions by creating a lifecycle configuration rule that applies to all objects in the bucket and specifies the expiration action for noncurrent versions. This way, you can reduce the storage costs of your S3 bucket without requiring any application changes or manual intervention. S3 Lifecycle runs once a day and marks the eligible object versions for deletion. You are no longer charged for the objects that are marked for deletion. S3 Lifecycle is the most cost-effective and simple solution among the options.

B. Use an AWS Lambda function to check for older versions and delete all but the two most recent versions. This option is not optimal because it requires you to write, test, and maintain a custom Lambda function that scans the S3 bucket for older versions and deletes them. This can incur additional costs for Lambda invocations and increase the operational complexity and overhead. Moreover, you need to ensure that your Lambda function has the appropriate permissions and error handling mechanisms to perform the deletion operation.

C. Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions. This option is not ideal because S3 Batch Operations is designed for performing large-scale operations on S3 objects, such as copying, tagging, restoring, or invoking a Lambda function. To use S3 Batch Operations to delete noncurrent object versions, you need to provide a manifest file that lists the object versions that you want to delete. This can be challenging and time-consuming to generate and update. Moreover, S3 Batch Operations charges you for each operation that you perform, which can increase your costs.

D. Deactivate versioning on the S3 bucket and retain the two most recent versions. This option is not feasible because deactivating versioning on an S3 bucket does not delete the existing object versions. Instead, it prevents new versions from being created. Therefore, you still need to delete the older versions manually or use another method to do so. Additionally, deactivating versioning can compromise the data protection and recovery capabilities of your S3 bucket.

Requirement Analysis: The application needs near real-time access to data, persistence, and minimal operational overhead.

ElastiCache for Redis: Provides in-memory data storage with persistence, supporting fast access and durability.

Operational Overhead: Managed service reduces the burden of setup, maintenance, and scaling.

Implementation:

Deploy an ElastiCache for Redis cluster.

Configure Redis to persist data to disk using AOF (Append-Only File) or RDB (Redis Database Backup) snapshots.

Conclusion: ElastiCache for Redis meets the requirements for fast access, data persistence, and low operational overhead.

References

Amazon ElastiCache:ElastiCache for Redis Documentation

S3 Intelligent-Tiering - Perfect use case when you don't know the frequency of access or irregular patterns of usage.

Amazon S3 offers a range of storage classes designed for different use cases. These include S3 Standard for general-purpose storage of frequently accessed data; S3 Intelligent-Tiering for data with unknown or changing access patterns; S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) for long-lived, but less frequently accessed data; and Amazon S3 Glacier (S3 Glacier) and Amazon S3 Glacier Deep Archive (S3 Glacier Deep Archive) for long-term archive and digital preservation. If you have data residency requirements that can’t be met by an existing AWS Region, you can use the S3 Outposts storage class to store your S3 data on-premises. Amazon S3 also offers capabilities to manage your data throughout its lifecycle. Once an S3 Lifecycle policy is set, your data will automatically transfer to a different storage class without any changes to your application.

https://aws.amazon.com/getting-started/hands-on/getting-started-using-amazon-s3-intelligent-tiering/?nc1=h_ls

Understanding the Requirement: The application running in a private subnet needs to store and retrieve data from S3 without data traveling over the public internet.

Analysis of Options:

NAT Gateway: Allows private subnets to access the internet but incurs additional costs and still routes traffic through the public internet.

AWS Storage Gateway: Provides hybrid cloud storage solutions but is not the most cost-effective for direct S3 access from within the VPC.

S3 Interface Endpoint: Provides private access to S3 but is generally used for specific use cases where more granular control is required, which might be overkill and more expensive.

S3 Gateway Endpoint: Provides private, cost-effective access to S3 from within the VPC without routing traffic through the public internet.

Best Solution:

S3 Gateway Endpoint: This option meets the requirements for secure, private access to S3 from a private subnet most cost-effectively.

Requirement Analysis: The database experiences peak traffic multiple times a day but has wasted capacity during non-peak hours. The goal is to reduce costs with minimal operational effort.

Aurora Serverless Overview: Aurora Serverless automatically adjusts database capacity based on current demand, scaling up during peak times and scaling down during non-peak times.

Cost Efficiency: Aurora Serverless charges only for the capacity used, which is more cost-effective than provisioning for peak traffic.

Operational Efficiency: Aurora Serverless eliminates the need for manual scaling or scheduling Lambda functions for capacity management.

Implementation: Migrate the database from the provisioned Aurora PostgreSQL cluster to an Aurora Serverless cluster.

References

Amazon Aurora Serverless:Aurora Serverless Documentation

Understanding the Requirement: The company needs programmatic access to its AWS usage costs for the current year and cost forecasts for the next 12 months, with minimal operational overhead.

Analysis of Options:

AWS Cost Explorer API: Provides programmatic access to detailed usage and cost data, including forecast costs. It supports pagination for handling large datasets, making it an efficient solution.

Downloadable AWS Cost Explorer report csv files: While useful, this method requires manual handling of files and does not provide real-time access.

AWS Budgets actions via FTP: This is less suitable as it involves setting up FTP transfers and does not provide the same level of detail and real-time access as the API.

AWS Budgets reports via SMTP: Similar to FTP, this method involves additional setup and lacks the real-time access and detail provided by the API.

Best Option for Minimal Operational Overhead:

AWS Cost Explorer API provides direct, programmatic access to cost data, including detailed usage and forecasting, with minimal setup and operational effort. It is the most efficient solution for integrating cost data into an operational cost dashboard.

(https://aws.amazon.com/cloudfront

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

Understanding the Requirement: The university wants to centralize management and automate backups for its AWS services (EC2, RDS, and DynamoDB), reducing reliance on custom scripts.

Analysis of Options:

Third-party backup software with AWS Storage Gateway: This solution introduces external dependencies and adds complexity compared to using native AWS services.

AWS Backup: Provides a centralized, fully managed service to automate and manage backups across various AWS services, including EC2, RDS, and DynamoDB.

AWS Config: Primarily used for compliance and configuration monitoring, not for backup management.

AWS Systems Manager State Manager: Useful for configuration management but not specifically designed for managing backups.

Best Solution:

AWS Backup: This service offers the necessary functionality to centralize and automate backups, providing a streamlined and integrated solution with minimal effort.

Understanding the Requirement: The company needs a container solution that can scale across on-premises, hybrid, and cloud environments, with a load balancer for HTTP traffic.

Analysis of Options:

Fargate Launch Type and ECS Anywhere: Using Fargate for cloud-based containers and ECS Anywhere for on-premises containers provides a unified management experience across environments without needing to manage infrastructure.

Application Load Balancer: Suitable for HTTP traffic and can distribute requests to the ECS services, ensuring scalability and performance.

Network Load Balancer: Typically used for TCP/UDP traffic, not specifically optimized for HTTP traffic.

EC2 Launch Type for ECS and ECS Anywhere with Fargate: Involves managing infrastructure for EC2 instances, increasing operational overhead.

Best Combination of Solutions:

ECS with Fargate Launch Type and ECS Anywhere: This provides flexibility and scalability across hybrid environments with minimal operational overhead.

Application Load Balancer: Optimized for HTTP traffic, ensuring efficient load distribution and scaling for the ECS services.

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

Understanding the Requirement: The company needs to ensure clients use a TLS certificate when accessing the website and automate the creation and renewal of TLS certificates.

Analysis of Options:

CloudFront Security Policy: Not applicable for creating certificates.

CloudFront Origin Access Control (OAC): Controls access to origins, not relevant for TLS certificate creation.

AWS Certificate Manager (ACM) with DNS Validation: Provides automated certificate management, including creation and renewal, with minimal manual intervention. DNS validation is automated and does not require manual intervention as email validation does.

AWS Certificate Manager (ACM) with Email Validation: Requires manual intervention to approve validation emails, which increases operational effort.

Best Solution:

AWS Certificate Manager (ACM) with DNS Validation: Ensures automated and efficient certificate management with the least operational effort.

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet.Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

Understanding the Requirement: The company needs all EC2 instances to share up-to-date website content with minimal lag time, running behind an Application Load Balancer.

Analysis of Options:

EC2 User Data with ALB: Complex and not scalable as it requires updating each instance manually.

Amazon EFS: Provides a scalable, shared file storage solution that can be mounted by multiple EC2 instances, ensuring all instances have access to the same up-to-date content.

Amazon S3 with EC2 Sync: Involves periodic synchronization which introduces lag and complexity.

Amazon EBS Snapshots: Not suitable for dynamic and frequent updates required by a content management system.

Best Solution:

Amazon EFS: Ensures all EC2 instances have access to a consistent and up-to-date set of website assets with minimal lag time, meeting the requirements effectively.

AWS DataSync is a data transfer service that simplifies, automates, and accelerates moving data between on-premises storage systems and AWS storage services over the internet or AWS Direct Connect1. AWS DataSync can transfer data to Amazon FSx for Windows File Server, which is a fully managed file system that is accessible over the industry-standard Server Message Block (SMB) protocol. Amazon FSx for Windows File Server is builton Windows Server, delivering a wide range of administrative features suchas user quotas, end-user file restore, and Microsoft Active Directory (AD) integration2. This solution meets the requirements of the question because:

It can migrate more than 100 TB of data to AWS within a reasonable time frame, as AWS DataSync is optimized for high-speed and efficient data transfer1.

It can preserve the intricate directory structure and the millions of small files stored in deep hierarchies of subfolders, as AWS DataSync can handle complex file structures and metadata, such as file names, permissions, and timestamps1.

It can avoid changing the applications to access the data after migration, as Amazon FSx for Windows FileServer supports the same SMB protocol and Windows Server features that the company’s on-premises file storage uses2.

It can reduce the operational overhead, as AWS DataSync and Amazon FSx for Windows File Server are fully managed services that handle the tasks of setting up, configuring, and maintaining the data transfer and the file system12.

This option is the most operationally efficient way to meet the requirements because it allows the company to monitor and analyze the database login activity across all the accounts in the organization. By publishing the Aurora general logs to a log group in Amazon CloudWatch Logs, the company can enable the logging of the database connections, disconnections, and failed authentication attempts. By exporting the log data to a central Amazon S3 bucket, the company can store the log data in a durable and cost-effective way and use other AWS services or tools to perform further analysis or alerting on the log data. For example, the company can use Amazon Athena to query the log data in Amazon S3, or use Amazon SNS to send notifications based on the log data.

A. Attach service control policies (SCPs) to the root of the organization to identify the failed login attempts. This option is not effective because SCPs are not designed to identify the failed login attempts, but to restrict the actions that the users and roles can perform in the member accounts of the organization. SCPs are applied to the AWS API calls, not to the database login attempts. Moreover, SCPs do not provide any logging or analysis capabilities for the database activity.

B. Enable the Amazon RDS Protection feature in Amazon GuardDuty for the member accounts of the organization. This option is not optimal because the Amazon RDS Protection feature in Amazon GuardDuty is not available for Aurora PostgreSQL databases, but only for Amazon RDS for MySQL and Amazon RDS for MariaDB databases. Moreover, the Amazon RDS Protection feature does not monitor the database login attempts, but the network and API activity related to the RDS instances.

D. Publish all the Aurora PostgreSQL database events in AWS CloudTrail to a central Amazon S3 bucket. This option is not sufficient because AWS CloudTrail does not capture the database login attempts, but only the AWS API calls made by or on behalf of the Aurora PostgreSQLdatabase. For example, AWS CloudTrail can record the events such as creating, modifying, or deleting the database instances, clusters, or snapshots, but not the events such as connecting, disconnecting, or failing to authenticate to the database.

Requirement Analysis: The company needs to scale throughput for uploading large files to S3 and downloading them to EC2 instances.

S3 Multipart Uploads: This method allows for the parallel upload of parts of a file, improving upload efficiency and reliability.

Parallel Fetching: Fetching multiple byte-ranges in parallel from S3 improves download performance.

Implementation:

For uploads, use the S3 multipart upload API to upload files in parallel.

For downloads, use the S3 API to request multiple byte-ranges concurrently.

Conclusion: These solutions effectively scale throughput and improve the performance of both uploads and downloads.

References

S3 Multipart Upload:Amazon S3 Multipart Upload

Parallel Fetching:S3 Byte-Range Fetches

This option is the best solution because it allows the company to migrate the container application to AWS with minimal changes and leverage a managed service to run the Kubernetes cluster and the message queue. By using Amazon EKS, the company can run the container application on a fully managed Kubernetes control plane that is compatible with the existingKubernetes tools and plugins. Amazon EKS handles the provisioning, scaling, patching, and security of the Kubernetes cluster, reducing the operational overhead and complexity. By using Amazon MQ, the company can use a fully managed message broker service that supports AMQP and other popular messaging protocols. Amazon MQ handles the administration, maintenance, and scaling of the message broker, ensuring high availability, durability, and security of the messages.

A. Migrate the container application to Amazon Elastic Container Service (Amazon ECS) Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not optimal because it requires the company to change the container orchestration platform from Kubernetes to ECS, which can introduce additional complexity and risk. Moreover, it requires the company to change the messaging protocol from AMQP to SQS, which can also affect the application logic and performance. Amazon ECS and Amazon SQS are both fully managed services that simplify the deployment and management of containers and messages, but they may not be compatible with the existing application architecture and requirements.

C. Use highly available Amazon EC2 instances to run the application Use Amazon MQ to retrieve the messages. This option is not ideal because it requires the company to manage the EC2 instances that host the container application. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, the company would need to install and maintain the Kubernetes software on the EC2 instances, which can also add complexity and risk. Amazon MQ is a fully managed message broker service that supports AMQP and other popular messaging protocols, but it cannot compensate for the lack of a managed Kubernetes service.

D. Use AWS Lambda functions to run the application Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not feasible because AWS Lambda does not support running container applications directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run container applications on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the container API, which can introduce additional complexity and overhead. Moreover, Lambda functions have limitations in terms of available CPU, memory, and runtime, which may not suit the application needs. Amazon SQS is a fully managed message queue service that supports asynchronous communication, but it does not support AMQP or other messaging protocols.

Understanding the Requirement: The company needs to monitor and notify changes to the OU hierarchy with minimal operational overhead.

Analysis of Options:

AWS Control Tower with Account Drift Notifications: AWS Control Tower provides automated account provisioning and governance, including drift detection and notifications for changes in the OU hierarchy.

AWS Control Tower with AWS Config: AWS Config provides resource configuration tracking but is more complex compared to drift notifications directly available in Control Tower.

AWS Service Catalog with CloudTrail: While CloudTrail tracks changes, setting up notification mechanisms involves more operational overhead.

AWS CloudFormation with Drift Detection: Suitable for tracking configuration changes but less efficient for monitoring OU hierarchy changes compared to Control Tower's built-in features.

Best Solution:

AWS Control Tower with Account Drift Notifications: Provides a streamlined and efficient way to detect and notify changes in the OU hierarchy with minimal operational overhead.

Understanding the Requirement: The company wants to improve caching to enhance website performance while ensuring that stale content is not served for more than a few minutes after a deployment.

Analysis of Options:

Set CloudFront TTL: Setting a short TTL (e.g., 2 minutes) ensures that cached content is refreshed frequently, reducing the risk of serving stale content.

S3 Bucket TTL: This would not control the cache duration for the CloudFront distribution.

Cache-Control Private: This directive is for controlling caching by private caches (e.g., browsers) and is not applicable for CloudFront.

Lambda@Edge: While this can add headers dynamically, it adds complexity and operational overhead.

Cache-Control max-age and CloudFront Invalidation: Setting a longer max-age for objects ensures they are cached longer, reducing load on the origin. Invalidation ensures that updated content is refreshed immediately after deployment.

Best Combination of Caching Methods:

Set the CloudFront default TTL to 2 minutes: This balances caching and freshness of content.

Add a Cache-Control max-age directive of 24 hours and use CloudFront invalidation: This ensures efficient caching while providing a mechanism to clear outdated content immediately after a deployment.

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices.Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.

This combination of actions will provide high availability and minimum latency for global users by using AWS Global Accelerator and Application Load Balancers. AWS GlobalAccelerator is a networking service that helps you improve the availability, performance, and security of your internet-facing applications by using the AWS global network. It provides two global static public IPs that act as a fixed entry point to your application endpoints, such as Application Load Balancers, in multiple Regions1. Global Accelerator uses the AWS backbone network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure. It also offers TCP and UDP support, traffic encryption, and DDoS protection2. Application Load Balancers are external load balancers that distribute incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones. They support both HTTP and HTTPS (SSL/TLS) protocols, and offer advanced features such as content-based routing, health checks, and integration with other AWS services3. By creatingexternal Application Load Balancers in front of the application in each Region, you can ensure that the application can handle varying load patterns and scale on demand. By creating an AWS Global Accelerator accelerator to route traffic to the load balancers in each Region, you can leverage the performance, security, and availability of the AWS global network to deliver the best possible user experience.

Requirement Analysis: Need quick, cost-effective, and consistent access to on-premises network services from multiple AWS accounts.

AWS Transit Gateway: Centralizes and simplifies network management by connecting VPCs and on-premises networks.

Direct Connect Integration: Assigning DX to the transit gateway ensures consistent and high-performance connectivity.

Operational Overhead: Minimal because Transit Gateway simplifies routing and management.

Implementation:

Set up AWS Transit Gateway.

Connect new AWS accounts to the Transit Gateway.

Route traffic through Transit Gateway to on-premises servers via Direct Connect.

Conclusion: This solution provides a scalable, cost-effective, and low-overhead method to meet connectivity requirements.

References

AWS Transit Gateway:AWS Transit Gateway Documentation

These options are the most suitable ways to design a shared storage solution for a web application that is deployed across multiple Availability Zones and requires strong consistency. Option B uses Amazon Elastic File System (Amazon EFS) as a shared file system that can be mounted on multiple EC2 instances in different Availability Zones. Amazon EFS provides high availability, durability, scalability, and performance for file-based workloads. It also supports strong consistency, which means that any changes made to the file system are immediately visible to all clients. Option E uses Amazon S3 as a shared object store that can store the web content and serve it through Amazon CloudFront, a content delivery network (CDN). Amazon S3 provides high availability, durability, scalability, and performance for object-based workloads. It also supports strong consistency for read-after-write and list operations, which means that any changes made to the objects are immediately visible to all clients. By setting the metadata for the Cache-Control header to no-cache, the web content can be prevented from being cached by the browsers or the CDN edge locations, ensuring that the latest content is always delivered to the users.

Option A is not suitable because using AWS Storage Gateway Volume Gateway as a shared storage solution for a web application is not efficient or scalable. AWS Storage Gateway Volume Gateway is a hybrid cloud storage service that provides block storage volumes that can be mounted on-premises or on EC2 instances as iSCSI devices. It is useful for migrating or backing up data to AWS, but it is not designed for serving web content or providing strong consistency. Moreover, using Volume Gateway would incur additional costs and complexity, and it would not leverage the native AWS storage services.

Option C is not suitable because creating a shared Amazon EBS volume and mounting it on multiple EC2 instances is not possible or reliable. Amazon EBS is a block storage service that provides persistent and high-performance volumes for EC2 instances. However, EBS volumescan only be attached to one EC2 instance at a time, and they are constrained to a single Availability Zone. Therefore, creating a shared EBS volume for a web application that is deployed across multiple Availability Zones is not feasible. Moreover, EBS volumes do not support strong consistency, which means that any changes made to the volume may not be immediately visible to other clients.

Option D is not suitable because using AWS DataSync to perform continuous synchronization of data between EC2 hosts in the Auto Scaling group is not efficient or scalable. AWS DataSync is a data transfer service that helps you move large amounts of data to and from AWS storage services. It is useful for migrating or archiving data, but it is not designed for serving web content or providing strong consistency. Moreover, using DataSync would incur additional costs and complexity, and it would not leverage the native AWS storage services. References:

What Is Amazon Elastic File System?

What Is Amazon Simple Storage Service?

What Is Amazon CloudFront?

What Is AWS Storage Gateway?

What Is Amazon Elastic Block Store?

What Is AWS DataSync?

Understanding the Requirement: The application running on EC2 instances in a private subnet needs to process sensitive information from an S3 bucket without using the internet.

Analysis of Options:

Internet Gateway: This would expose the application to the internet, which is not suitable for accessing sensitive information securely.

VPN Connection: VPN is primarily used for secure connections between on-premises networks and AWS VPCs, not for direct S3 access within the same VPC.

NAT Gateway: This allows instances in a private subnet to connect to the internet, but the goal is to avoid internet access.

VPC Endpoint: Provides a private connection between the VPC and S3 without using the internet, ensuring secure access to the S3 bucket.

Best Solution:

VPC Endpoint: Configuring a VPC endpoint allows secure, private communication between the EC2 instances and the S3 bucket without using the internet, ensuring data security and compliance.

This solution meets the requirements because it allows the company to track and manage its cloud spending by using cost allocation tags to assign costs to different departments, creating usage budgets to set spending limits, and adding alert thresholds to receive notifications when the spending reaches a certain percentage of the budget. This way, the company can monitor its experimental workloads and avoid overspending on the cloud.

This JSON text is an identity-based policy that grants specific permissions. The IAM principals that the solutions architect can attach this policy to are Role and Group. This is because the policy is written in JSON and is an identity-based policy, which can be attached to IAM principals such as users, groups, and roles. Identity-based policies are permissions policies that you attach to IAM identities (users, groups, or roles) and explicitly state what that identity is allowed (or denied) to do1. Identity-based policies are different from resource-based policies, which define the permissions around the specific resource1. Resource-based policies are attached to a resource, such as an Amazon S3 bucket or anAmazon EC2 instance1. Resource-based policies can also specify a principal, which is the entity that is allowed or denied access to the resource1. Organization is not an IAM principal, but a feature of AWS Organizations that allows you to manage multiple AWSaccountscentrally2. Amazon ECS resource and Amazon EC2 resource are not IAM principals, but AWS resources that can have resource-based policies attached to them34.

To clone the production data into the test environment with high I/O performance and without affecting the production environment, the best option is to take EBS snapshots of the production EBS volumes and restore them onto new EBS volumes in the testenvironment. Then, attach the new EBS volumes to EC2 instances in the test environment. This option minimizes the time required to clone the data and ensures that modifications to the cloned data do not affect the production environment. Therefore, option C is the correct answer.

AWS Secrets Manager helps you protect secrets needed to access your applications, services, and IT resources. The service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle.

https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html

Secrets Manager enables you to replace hardcoded credentials in your code, including passwords, with an API call to Secrets Manager to retrieve the secret programmatically. This helps ensure the secret can't be compromised by someone examining your code, because the secret no longer exists in the code. Also, you can configure Secrets Manager to automatically rotate the secret for you according to a specified schedule. This enables you to replace long-term secrets with short-term ones, significantly reducing the risk of compromise.

DynamoDB Global Tablesprovide a fully managed, multi-region, and multi-master database solution that allows you to deploy DynamoDB tables in multiple AWS Regions. This ensures high availability and resiliency across different geographical locations, providing a seamlessgaming experience for users. Usingprovisioned capacity modewithauto-scalingensures cost-efficiency by scaling up or down based on actual demand.

Option A: While on-demand capacity mode is flexible, provisioned capacity with auto-scaling is more cost-effective for predictable workloads.

Option B (DAX): DAX improves read performance, but it doesn't provide the multi-region replication needed for high availability and resiliency.

Option C: DynamoDB Streams with manual cross-region replication adds more complexity and operational overhead compared to Global Tables.

AWS References:

DynamoDB Global Tables

To design the API Gateway URL with the company's domain name and corresponding certificate, the company needs to do the following: 1. Create a Regional API Gateway endpoint: This will allow the company to create an endpoint that is specific to a region. 2. Associate the API Gateway endpoint with the company's domain name: This will allow the company to use its own domain name for the API Gateway URL. 3. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the same Region: This will allow the company to use HTTPS for secure communication withits APIs. 4. Attach the certificate to the API Gateway endpoint: This will allow the company to use the certificate for securing the API Gateway URL. 5. Configure Route 53 to route traffic to the API Gateway endpoint: This will allow the company to use Route 53 to route traffic to the API Gateway URL using the company's domain name.

AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and AWS storage services. AWS DataSync can transfer data at speeds up to 10 times faster than open-source tools by using a purpose-built network protocol and parallelizing data transfers. AWS DataSync also handles encryption, data integrity verification, and bandwidth optimization. To use AWS DataSync, users need to deploy a DataSync agent on their on-premises servers, which connects to the NFS servers and syncs the data to Amazon S3. Users can schedule periodic or one-time sync tasks and monitor the progress and status of the transfers.

The other options are not correct because they are either not cost-effective or not suitable for the use case. Setting up AWS Glue to copy the data from the on-premises servers to Amazon S3 is not cost-effective because AWS Glue is a serverless data integration service that is mainly used for extract, transform, and load (ETL) operations, not for simple data backup. Setting up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3 is not cost-effective because AWS Transfer for SFTP is a fully managed service that provides secure file transfer using the SFTP protocol, which is more suitable for exchanging data with third parties than for backing up data. Setting up an AWS Direct Connect connection between the on-premises data center and a VPC, and copying the data to Amazon S3 is not cost-effective because AWS Direct Connect is a dedicated network connection between AWS and the on-premises location, which has high upfront costs and requires additional configuration.

Requirement Analysis: The application must process each credit card data validation request at least once, securely and cost-effectively.

SQS FIFO Queues: Ensures that each message is processed exactly once and in the exact order sent.

AWS Lambda: Using Lambda for event-driven processing ensures scalability and cost-efficiency.

SSE-SQS: Provides encryption at rest using SQS-managed keys, simplifying encryption management.

Implementation:

Set up SQS FIFO queues as the event source for Lambda.

Enable SSE-SQS for encryption.

Ensure the Lambda execution role has the necessary permissions to use the encryption keys.

Conclusion: This combination meets the requirements of security, exact-once processing, and cost-effectiveness.

References

Amazon SQS:Amazon SQS Documentation

AWS Lambda with SQS:Using AWS Lambda with Amazon SQS

AmazonTextractcan extract text from the PDFs, andAmazon Comprehendis the most suitable service to analyze the extracted text for sentiment and insights. Comprehend offers a fully managed, low-operational overhead solution for analyzing text data. The results can then be stored in anAmazon S3bucket, ensuring scalability and easy access.

Option A: Athena is for querying structured data and is not suitable for sentiment analysis.

Option B: SageMaker adds complexity and is not necessary when Comprehend can handle sentiment analysis natively.

Option D: QuickSight is used for visualization and analytics, but it does not provide sentiment analysis.

AWS References:

Amazon Comprehend

Amazon Textract

 This solution meets the requirements with the least operational overhead because it uses AWS services that are fully managed and scalable. The EventBridge rule can detect the DeleteKey operation from the AWS KMS API and trigger the Systems Manager Automation runbook, which can execute a predefined workflow to cancel the key deletion. The EventBridge rule can also publish an SNS message to the topic that sends an email notification to the administrators. This way, the company can prevent the accidental deletion of KMS keys and notify the administrators of any attempts to delete them.

Option A is not a valid solution because AWS Config rules are used to evaluate the configuration of AWS resources, not to cancel the deletion of KMS keys. Option B is not a valid solution because it requires creating and maintaining a custom Lambda function that has logic to prevent KMS key deletion, which adds operational overhead. Option D is not a valid solution because it only notifies the administrators of the DeleteKey operation, but does not cancel it.

AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources. AWS WAF allows users to create rules that block, allow, or count web requests based on customizable web security rules. One of the types of rules that can be created is an SQL injection rule, which allows users to specify a list of IP addresses or IP address ranges that they want to allow or block. By using AWS WAF to protect the application, the company can prevent SQL injection and other web-based attacks from reaching the application and the database.

RDS parameter groups are collections of parameters that define how a database instance operates. Users can modify the parameters in a parameter group to change the behavior and performance of the database. By using RDS parameter groups to configure the security settings, the company can enforce best practices such as disabling remote root login, requiring SSL connections, and limiting the maximum number of connections.

The other options are not correct because they do not effectively protect the application and the database from SQL injection and other web-based attacks. Using security groups and network ACLs to secure the database and application servers is not sufficient because they only filter traffic at the network layer, not at the application layer. Using AWS Network Firewall to protect the application and the database is not necessary because it is a stateful firewall service that provides network protection for VPCs, not for individual applications or databases. Using different database accounts in the application code for different functions is a good practice, but it does not prevent SQL injection attacks from exploiting vulnerabilities in the application code.

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_MySQL.Replication.ReadReplicas.html

AmazonEventBridgeAPI destinations allow you to send data from AWS to external systems, like Salesforce, using HTTP APIs, including those secured with OAuth. This provides a secure and scalable solution for sending messages from the order processing application to Salesforce.

Option A and B (SNS): SNS is not ideal for OAuth-secured external APIs and lacks the necessary OAuth integration.

Option D (MSK): Amazon MSK is a Kafka-based streaming solution, which is overkill for simple message forwarding to Salesforce.

AWS References:

Amazon EventBridge API Destinations

A Network Load Balancer can be assigned one Elastic IP address for each Availability Zone it uses1. This allows the clients to reach the load balancer using a static IP address that can be authorized on their firewalls. An Application Load Balancer cannot be assigned an Elastic IP address2. An A record in an Amazon Route 53 hosted zone pointing to an Elastic IP address would not work because the load balancer would still use its own IP address as the source of the forwarded requests to the web service. An EC2 instance with a public IP address running as a proxy in front of the load balancer would add unnecessary complexity and cost, and would not provide the same scalability and availability as a Network Load Balancer. References: 1: Network Load Balancers - Elastic Load Balancing3, IP address type section2: How to assign Elastic IP to Application Load Balancer in AWS?4, answer section.

This solution meets the requirements because it allows the company to move the application to a fully managed service without managing any servers or storage infrastructure. AWS Fargate is a serverless compute engine for containers that runs the Amazon ECS tasks. With Fargate, the company does not need to provision, configure, or scale clusters of virtual machines to run containers. Amazon EFS is a fully managed file system that can be accessed by multiple containers concurrently. With EFS, the company does not need to provision and manage storage capacity. EFS provides a simple interface to create and configure file systems quickly and easily. The company can use the EFS volume as a persistent storage volume mounted in the containers to store the persistent data. The company can also use the EFS mount helper to simplify the mountingprocess. References: Amazon ECS on AWS Fargate, Using Amazon EFS file systems with Amazon ECS, Amazon EFS mount helper.

To ensure that the shopping cart data is preserved at all times, a solutions architect should configure Amazon ElastiCache for Redis to cache catalog data from Amazon DynamoDB and shopping cart data from the user’s session. This solution has the following benefits:

It offers the lowest possible latency from the application, as ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications1.

It scales with traffic volume, as ElastiCache for Redis supports horizontal scaling by adding more nodes or shards to the cluster, and vertical scaling by changing the node type2.

It ishighly available, as ElastiCache for Redis supports replication across multiple Availability Zones and automatic failover in case of a primary node failure3.

It preserves user session data even if the user is disconnected and reconnects, as ElastiCache for Redis can store session data, such as user login information and shopping cart contents, in a persistent and durable manner using snapshots or AOF (append-only file) persistence4.

This solution meets the requirements most securely because it uses IAM roles and the STS AssumeRole API operation to authenticate and authorize the inventory application to access the DynamoDB tables in different accounts. IAM roles are more secure than IAM users or certificates because they do not require long-term credentials or passwords. Instead, IAM roles provide temporary security credentials that are automatically rotated and can be configured with a limited duration. The STS AssumeRole API operation enables you to request temporary credentials for a role that you are allowed to assume. By using this operation, you can delegate access to resources that are in different AWS accounts that you own or that are owned by third parties. The trust policy of the role defines which entities can assume the role, and the permissions policy of the role defines which actions can be performed on the resources. By using this solution, you can avoid hard-coding credentials or certificates in the inventory application, and you can also avoid storing them in Secrets Manager or ACM. You can also leverage the built-in security features of IAM and STS, such as MFA, access logging, and policy conditions.

The solution that meets the requirements with the most operational efficiency is to configure public subnets in the existing VPC and deploy an MSK cluster in the public subnets. This solution allows the data ingestion solution to be publicly available over the internet without creating a new VPC or deploying a load balancer. The solution also ensures that the data intransit is encrypted by enabling mutual TLS authentication, which requires both the client and the server to present certificates for verification. This solution leverages the public access feature of Amazon MSK, which is available for clusters running Apache Kafka 2.6.0 or later versions1.

The other solutions are not as efficient as the first one because they either create unnecessary resources or do not encrypt the data in transit. Creating a new VPC with public subnets would incur additional costs and complexity for managing network resources and routing. Deploying an ALB or an NLB would also add more costs and latency for the data ingestion solution. Moreover, an ALB or an NLB would not encrypt thedata in transit by itself, unless they are configured with HTTPS listeners and certificates, which would require additional steps and maintenance. Therefore, these solutions are not optimal for the given requirements.

Configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule is the best option because it allows for the proactive scaling of the EC2 instances before the monthly batch run begins. This will ensure that the application is able to handle the increased workload without experiencing downtime. The scheduled scaling policy can be configured to increase the number of instances in the Auto Scaling group a few hours before the batch run and then decrease the number of instances after the batch run is complete. This will ensure that the resources are available when needed and not wasted when not needed. The most appropriate solution to handle the increased workload during the monthly batch run and avoid downtime would be to configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-scheduled-scaling.html

This option is the mostefficient because it sets an overall password policy for the entire AWS account, which is a way to specify complexity requirements and mandatory rotationperiods for IAM user passwords1. It also meets the requirement of setting a password policy for all new users, as the password policy applies to all IAM users in the account. This solution meets the requirement of setting specific complexity requirements and mandatory rotation periods for IAM user passwords. Option B is less efficient because it sets a password policy for each IAM user in the AWS account, which is not possible as password policies can only be set at the account level. Option C is less efficient because it uses third-party vendor software to set password requirements, which is not necessary as IAM provides a built-in way to set password policies. Option D is less efficient because it attaches an Amazon CloudWatch rule to the Create_newuser event to set the password with the appropriate requirements, which is not possible as CloudWatch rules cannot modify IAM user passwords.

This solution meets the requirements most cost-effectively because it leverages the serverless and event-driven capabilities of AWS Lambda and Amazon EventBridge. AWS Lambda allows you to run code without provisioning or managing servers, and you pay only for the compute time you consume. Amazon EventBridge is a serverless event bus service that lets you connect your applications with data from various sources and routes that data to targets such as AWS Lambda. By using Amazon EventBridge, you can create a rule that triggers a Lambda function to run the program when reports are requested, and you can also schedule the rule to run during the last week of each month. This way, you can generate reports in the least amount of time and pay only for the resources you use.

Amazon Kinesis Data Streams is a scalable and reliable service that can ingest, buffer, and process streaming data in real-time. It can handle large traffic spikes and preserve the order ofthe incoming data records. AWS Lambda is a serverless compute service that can process the data streams from Kinesis Data Streams without requiring any infrastructure management. It can also scale automatically to match the throughput of the data stream. Amazon DynamoDB is a fully managed, highly available, and fast NoSQL database that can store the processed updates from Lambda. It can also handle high write throughput and provide consistent performance. By using these services, the solutions architect can design a solution that meets the requirements of the company with the least operational overhead.

AWS Cost Explorer is a tool that enables you to view and analyze your costs and usage. You can explore your usage and costs using the main graph, the Cost Explorer cost and usage reports, or the Cost Explorer RI reports. You can view data for up to the last 12 months, forecast how muchyou're likely to spend for the next 12 months, and get recommendations for what Reserved Instances to purchase. You can use Cost Explorer to identify areas that need further inquiry and see trends that you can use to understand your costs.https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html

AWS Control Tower is a fully managed service that simplifies the setup and governance of a secure, compliant, multi-account AWS environment. It establishes a landing zone that is based on best-practices blueprints, and it enables governance using controls you can choose from a pre-packaged list. The landing zone is a well-architected, multi-account baseline that follows AWS best practices. Controls implement governance rules for security, compliance, and operations. AWS Security Hub is a service that provides a comprehensive view of your security posture across your AWS accounts. It aggregates, organizes, and prioritizes security alerts and findings from multiple AWS services, such as Amazon GuardDuty, Amazon Inspector, Amazon Macie, AWS Firewall Manager, and AWS IAM Access Analyzer, as well as from AWS Partner solutions. AWS Security Hub continuously monitors your environment using automatedcompliance checks based on the AWS best practices and industry standards, such as the AWS Foundational Security Best Practices (FSBP) standard. AWS Control Tower Account Factory is a feature that automates the provisioning of new AWS accounts that are preconfigured to meet your business, security, and compliance requirements. By deploying an AWS Control Tower environment in the Organizations management account, you can leverage the existing organization structure and policies, and enable AWS Security Hub and AWS Control Tower Account Factory in the environment. This way, you can audit all API calls and logins in any existing or new AWS account, monitor the compliance status of each account with the FSBP standard, and provision new accounts with ease and consistency. This solution meets the requirements with the least operational overhead, as you do not need to manage any infrastructure, perform any data migration, or submit any requests for changes.

The most suitable solution for the company’s requirements is to configure AWS Glue DataBrew to transform the data and share the transformation steps with employees by using DataBrew recipes. This solution will provide a prebuilt solution for data transformation that does not require code, and will also provide data lineage and data profiling. The company can easily share the data transformation steps with employees throughout the company by using DataBrew recipes.

AWS Glue DataBrew is a visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data for analytics or machine learning by up to 80% faster. Users can upload their data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon Aurora, or Glue Data Catalog, and use a point-and-click interface to apply over 250 built-in transformations. Users can also preview the results of each transformation step and see how it affects the quality and distribution of the data1.

A DataBrew recipe is a reusable set of transformation steps that can be applied to one or more datasets. Users can create recipes from scratch or use existing ones from the DataBrew recipe library. Users can also export, import, or share recipes with other users or groups within their AWS account or organization2.

DataBrew also provides data lineage and data profiling features that help users understand and improve their data quality. Data lineage shows the source and destination of each dataset and how it is transformed by each recipe step. Data profiling shows various statistics and metrics about each dataset, such as column

These options are the best solutions because they allow the company to run the application with Docker containers in the AWS Cloud using a managed service that scales automatically and does not require any infrastructure to manage. By using AWS Fargate, the company can launch and run containers without having to provision, configure, or scale clusters of EC2 instances. Fargate allocates the right amount of compute resources for each container and scales them up or down as needed. By using Amazon ECS or Amazon EKS, the company can choose the container orchestration platform that suits its needs. Amazon ECS is a fully managed service that integrates with other AWS services and simplifies the deployment and management of containers. Amazon EKS is a managed service that runs Kubernetes on AWS and provides compatibility with existing Kubernetes tools and plugins.

C. Provision an Amazon API Gateway API Connect the API to AWS Lambda to run the containers. This option is not feasible because AWS Lambda does not support running Docker containers directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run Docker containers on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the Docker API, which can introduce additional complexity and overhead.

D. Use Amazon Elastic Container Service (Amazon ECS) with Amazon EC2 worker nodes. This option is not optimal because it requires the company to manage the EC2 instances that host the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs.

E. Use Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 worker nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs.

To provide the most high-performing experience for the users of the application, a solutions architect should use a latency routing policy for the Route 53 A record. This policy allows Route 53 to route traffic to the AWS Region that provides the lowest possible latency for the users1. A latency routing policy can also improve the availability of the application, as Route 53 can automatically route traffic to another Region if the primary Region becomes unavailable2.

The company wants to improve the availability and performance of the application, as well as protect it against common web exploits. The company also needs static IP addresses for the application. To meet these requirements, a solutions architect should recommend the following solution:

Put the EC2 instances behind Network Load Balancers (NLBs) in each Region. NLBs are designed to handle millions of requests per second while maintaining high throughput at ultra-low latency. NLBs also support static IP addresses for each Availability Zone, which can be useful for whitelisting or firewalling purposes.

Deploy AWS WAF on the NLBs. AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect availability, security, or performance. AWS WAF lets you define customizable web security rules that control which traffic to allow or block to your web applications.

Create an accelerator using AWS Global Accelerator and register the NLBs as endpoints. AWS Global Accelerator is a service that improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in any AWS Region. It uses the AWS global network to optimize the path from your users to your applications, improving the performance of your TCP and UDP traffic.

This solution will provide high availability across Availability Zones and Regions, improve performance by routing traffic over the AWS global network, protect the application from common web attacks, and provide static IP addresses for the application.

This solution meets the following requirements:

It is operationally efficient, as it only requires one transit gateway and one transit VIF to connect the Direct Connect connection to all the VPCs in the same AWS Region. The transit gateway acts as a regional network hub that simplifies the network management and reduces the number of VIFs and gateways needed.

It is scalable, as it can support up to 5000 attachments per transit gateway, which can include VPCs, VPNs, Direct Connect gateways, and peering connections. The transit gateway can also be connected to other transit gateways in different Regions or accounts using peering connections, enabling cross-Region and cross-account connectivity.

It is flexible, as it allows each VPC to communicate with all other VPCs and on-premises networks using dynamic routing protocols such as Border Gateway Protocol (BGP). The transit gateway’s route propagation feature automatically propagates the routes from the attached VPCs and VPNs to the transit gateway route table, eliminating the need to manually update the route tables.

To meet the requirements of the use case in a cost-effective way, the following steps are recommended:

Deploy an AWS Storage Gateway on premises in Amazon S3 File Gateway mode. This will allow the company to write the .csv files generated by the devices to an SMB file share, which will be stored as objects in Amazon S3 buckets. AWS Storage Gateway is a hybrid cloud storage service that integrates on-premises environments with AWS storage. Amazon S3 File Gateway mode provides a seamless way to connect to Amazon S3 and access a virtually unlimited amount of cloud storage1.

Set up an AWS Glue crawler to create a table based on the data that is in Amazon S3. This will enable the company to use standard SQL to query the data stored in Amazon S3 buckets. AWS Glue is a serverless data integration service that simplifies data preparation and analysis. AWS Glue crawlers can automatically discover and classify data from various sources, and create metadata tables in the AWS Glue Data Catalog2. The Data Catalog is a central repository that stores information about data sources and how to access them3.

Set up Amazon Athena to query the data that is in Amazon S3. This will provide the company analysts with a serverless and interactive query service that can analyze data directly in Amazon S3 using standard SQL. Amazon Athena is integrated with the AWS Glue Data Catalog, so users can easily point Athena at the data source tables defined by the crawlers. Amazon Athenacharges only for the queries that are run, and offers a pay-per-query pricing model, which makes it a cost-effective option for periodic queries4.

The other options are not correct because they are either not cost-effective or not suitable for the use case. Deploying an AWS Storage Gateway on premises in Amazon FSx File Gateway mode is not correct because this mode provides low-latency access to fully managed Windows file shares in AWS, which is not required for the use case. Setting up an Amazon EMR cluster with EMR File System (EMRFS) to query the data that is in Amazon S3 is not correct because this option involves setting up and managing a cluster of EC2 instances, which adds complexity and cost to the solution. Setting up an Amazon Redshift cluster to query the data that is in Amazon S3 is not correct because this option also involves provisioning and managing a cluster of nodes, which adds overhead and cost to the solution.

To address the issue of bandwidth limitations on the company's on-premises application, and to minimize the impact on internal user connectivity, a new AWS Direct Connect connection should be established to direct backup traffic through this new connection. This solution will offer a secure, high-speed connection between the company's data center and AWS, which will allow the company to transfer data quickly without consuming internet bandwidth.

AWS Fargate is a serverless compute engine that lets users run containers without having to manage servers or clusters of Amazon EC2 instances1. Users can use AWS Fargate on Amazon Elastic Container Service (Amazon ECS) to run the containerized web application with Service Auto Scaling. Amazon ECS is a fully managed container orchestration service that supports both Docker and Kubernetes2. Service Auto Scaling is a feature that allows users to adjust the desired number of tasks in an ECS service based on CloudWatch metrics, such as CPU utilization or request count3. Users can use AWS Fargate on Amazon ECS to migrate the application to AWS with minimum code changes and minimum development effort, as they only need to package their application in containers and specify the CPU and memory requirements.

Users can also use an Application Load Balancer to distribute the incoming requests. An Application Load Balancer is a load balancer that operates at the application layer and routes traffic to targets based on the content of the request. Users can register their ECS tasks as targets for an Application Load Balancer and configure listener rules to route requests to different target groups based on path or host headers. Users can use an Application Load Balancer to improve the availability and performance of their web application.

Amazon Inspector is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS. Amazon Inspector automatically assesses applications for exposure, vulnerabilities, and deviations from best practices. After performing an assessment, Amazon Inspector produces a detailed list of security findings prioritized by level of severity1. Amazon Inspector can scan the EC2 instances for software vulnerabilities and provide a report of each instance’s patch status. AWS Systems Manager Patch Manager is a capability of AWS Systems Manager that automates the process of patching managed nodes with both security-related updates and other types of updates. Patch Manager uses patch baselines, which include rules for auto-approving patches within days of their release, in addition to optional lists of approved and rejected patches. Patch Manager can patch fleets of Amazon EC2 instances, edge devices, on-premises servers, and virtual machines (VMs) by operating system type2. PatchManager can patch the EC2 instances on a regular schedule and provide a report of each instance’s patch status. Therefore, the combination of Amazon Inspector and AWS Systems Manager Patch Manager will meet the requirements of the question.

The other options are not valid because:

Amazon Macie is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Amazon Macie does not scan the EC2 instances for software vulnerabilities, but rather for data classification and protection3. A cron job is a Linux command for scheduling a task to be executed sometime in the future. A cron job is not a reliable way to patch the EC2 instances on a regular schedule, as it may fail or be interrupted by other processes4.

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect your AWS accounts and workloads. Amazon GuardDuty does not scan the EC2 instances for software vulnerabilities, but rather for network and API activity anomalies5. AWS Systems Manager Session Manager is a fully managed AWS Systems Manager capability that lets you manage your Amazon EC2 instances, edge devices, on-premises servers, and virtual machines (VMs) through an interactive one-click browser-based shell or the AWS Command Line Interface (AWS CLI). Session Manager does not patch theEC2 instances on a regular schedule, but rather provides secure and auditable node management2.

Amazon Detective is a security service that makes it easy to analyze, investigate, and quickly identify the root cause of potential security issues or suspicious activities. Amazon Detective does not scan the EC2 instances for software vulnerabilities, but rather collects and analyzes data from AWS sources such as Amazon GuardDuty, Amazon VPC Flow Logs, and AWS CloudTrail. Amazon EventBridge is a serverless event bus that makes it easy to connect applications using data from your own applications, integrated Software-as-a-Service (SaaS) applications, and AWS services. EventBridge delivers a stream of real-time data from event sources, such as Zendesk, Datadog, or Pagerduty, and routes that data to targets like AWS Lambda. EventBridge does not patch the EC2 instances on a regular schedule, but rather triggers actions based on events.

This solution meets the following requirements:

It is the least effort, as it does not require any additional AWS services, custom scripts, or data processing steps. Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. You can use Athena to query CloudTrail logs directly from the S3 bucket where they are stored, without any data loading or transformation. You can also use the AWS Management Console, the AWS CLI, or the Athena API to run and manage your queries.

It is effective, as it allows you to filter, aggregate, and join CloudTrail log data using SQL syntax. You can use various SQL functions and operators to specify the criteria for identifyingAccess Denied and Unauthorized errors, such as the error code, the user identity, the event source, the event name, the event time, and the resource ARN. You can also use subqueries, views, and common table expressions to simplify and optimize your queries.

It is flexible, as it allows you to customize and save your queries for future use. You can also export the query results to other formats, such as CSV or JSON, or integrate them with other AWS services, such as Amazon QuickSight, for further analysis and visualization.

The destination of your Kinesis Data Firehose delivery stream. Kinesis Data Firehose can send data records to various destinations, including Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service, and any HTTP endpoint that is owned by you or any of your third-party service providers. The following are the supported destinations:

* Amazon OpenSearch Service

* Amazon S3

* Datadog

* Dynatrace

* Honeycomb

* HTTP Endpoint

* Logic Monitor

* MongoDB Cloud

* New Relic

* Splunk

* Sumo Logic

https://docs.aws.amazon.com/firehose/latest/dev/create-name.html

https://aws.amazon.com/kinesis/data-streams/

Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service. KDS can continuously capture gigabytes of data per second fromhundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events.

To resolve the issue of slow page loads for a rapidly growing e-commerce website hosted on AWS, a solutions architect can take the following two actions:

1. Set up an Amazon CloudFront distribution

2. Create a read replica for the RDS DB instance

Configuring an Amazon Redshift cluster is not relevant to this issue since Redshift is a data warehousing service and is typically used for the analytical processing of large amounts of data.

Hosting the dynamic web content in Amazon S3 may not necessarily improve performance since S3 is an object storage service, not a web application server. While S3 can be used to host static web content, it may not be suitable for hosting dynamic web content since S3 doesn't support server-side scripting or processing.

Configuring a Multi-AZ deployment for the RDS DB instance will improve high availability but may not necessarily improve performance.

https://aws.amazon.com/es/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/

The correct answer is Option D. Deploy an S3 VPC gateway endpoint into the VPC and attach an endpoint policy that allows access to the S3 buckets. By deploying an S3 VPC gateway endpoint, the application can access the S3 buckets over a private network connection within the VPC, eliminating the need for data transfer over the internet. This can help reduce data transfer fees as well as improve the performance of the application. The endpoint policy can be used to specify which S3 buckets the application has access to.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-mixed-instances-groups.html

 Security groups are virtual firewalls that protect AWS instances and can be applied to EC2, ELB and RDS1. Security groups have rules for inbound and outbound traffic and are stateful, meaning that responses to allowed inbound traffic are allowed to flow out of the instance2. Network ACLs are different from security groups in several ways. They cover entire subnets, not individual instances, and are stateless, meaning that they require rules for both inbound and outbound traffic2. Network ACLs also support deny rules, while security groups only support allow rules2.

To meet the requirements of the scenario, the solutions architect should create two security groups: one for the DB instance and one for the web servers in the public subnet. The security group for the DB instance should allow traffic from the public subnet CIDR block on port 3306, which is the default port for MySQL3. This way, only the web servers in the public subnet can access the DB instance on that port. The security group for the web servers should allow traffic from 0 0 0 O’O on port 443, which is the default port forHTTPS4. This way, the web servers can accept secure connections from the internet on that port.

This option is the most efficient because it uses a predictive scaling policy for the Auto Scaling group, which isa type of scaling policy that uses machine learning to predict capacity requirements based on historical data from CloudWatch1. It also configures the policy to scale based on forecast, which enables the Auto Scaling group to adjust its capacity in advance of trafficchanges. It also sets the scaling metric to CPU utilization and the target value for the metric to 60%, which aligns with the baseline CPU utilization that is noted on each run. It also sets the instances to pre-launch 30 minutes before the jobs run, which ensures that enough capacity is provisioned before the weekly scripted batch jobs start. This solution meets the requirement of provisioning the capacity 30 minutes before the jobs run with the leastoperational overhead. Option A is less efficient because it uses a dynamic scaling policy for the Auto Scaling group, which is a type of scaling policy that adjusts your Auto Scaling group’s capacity in response to changing demand2. However, this does not provide a way to provision the capacity 30 minutes before the jobs run, as it only reacts to changing traffic. Option B is less efficientbecause it uses a scheduled scaling policy for the Auto Scaling group, which is a type of scaling policy that lets you scale your Auto Scaling group based on a schedule that you create3. However, this does not provide a way to scale based on forecast or CPU utilization, as it only scales based on predefined metrics and policies. Option D is less efficient because it uses an Amazon EventBridge event to invoke an AWS Lambda function when the CPU utilization metric value for the Auto Scaling group reaches 60%, which is a way to trigger serverless functions based on events. However, this does not provide a way to provision the capacity 30 minutes before the jobs run, as it only reacts to changing traffic.

SCPs are a type of organization policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization. SCPs help you to ensure your accounts stay within your organization’s access control guidelines1.

To apply an SCP to a specific set of accounts, you need to create an OU for those accounts and attach the SCP to the OU. This way, the SCP affects only the member accounts in that OU and not the other accounts in the organization. If you attach the SCP to the root OU, it will apply to all accounts in the organization, including the production account, which is not the desired outcome. If you attach the SCP to the management account, it will have no effect, as SCPs do not affect users or roles in the management account1.

Therefore, the best solutions to deploy the SCP are B and E. Option B attaches the SCP directly to the three nonproduction accounts, while option E creates a separate OU for the nonproductionaccounts and attaches the SCP to the OU. Both options will achieve the same result of restricting the EC2instance types in the nonproduction accounts, but option E might be more scalable and manageable if there are more accounts or policies to be applied in the future2.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/AmazonEFS.html Amazon FSx for Windows File Server provides fully managed Microsoft Windows file servers, backed by a fully native Windows file system.https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

Deletion protection is a feature of DynamoDB that prevents accidental deletion of tables. When deletion protection is enabled, you cannot delete a table unless you explicitly disable it first. This adds an extra layer of security and reduces the risk of data loss and operational disruption. Deletion protection is easy to enable and disable using the AWS Management Console, the AWSCLI, or the DynamoDB API. This solution has the least operational overhead, as you do not need to create, manage, or invoke any additional resources or services. References:

Using deletion protection to protect your table

Preventing Accidental Table Deletion in DynamoDB

Amazon DynamoDB now supports table deletion protection

A. Amazon EFS:Provides a scalable, shared file storage solution with minimal operational overhead. It's ideal for Linux-based workloads.

B. Amazon FSx for NetApp ONTAP:More suited for workloads requiring NetApp-specific features.

C. Amazon EBS:Requires manual management of file shares, which increases operational overhead.

D. Amazon FSx for Windows File Server:Best suited for Windows SMB workloads with low operational overhead.

E. Amazon FSx for OpenZFS:Better for Linux and Unix-based workloads.

AWS Storage Gateway is a hybrid cloud storage service that allows you to seamlessly integrate your on-premises applications with AWS cloud storage. Volume Gateway is a type of Storage Gateway that presents cloud-backed iSCSI block storage volumes to your on-premises applications. Volume Gateway operates in either cache mode or stored mode. In cache mode, your primary data is stored in Amazon S3, while retaining your frequently accessed data locally in the cache for low latency access. In stored mode, your primary data is stored locally and your entire dataset is available for low latency access on premises while also asynchronously getting backed up to Amazon S3.

For the pharmaceutical company’s use case, cache mode is the most suitable option, as it meets the following requirements:

It reduces the need to scale the on-premises storage infrastructure, as most of the data is stored in Amazon S3, which is scalable, durable, and cost-effective.

It provides low latency access to the subset of the data that the researchers regularly require, as it is cached locally in the Storage Gateway appliance.

It does not require the entire dataset to be accessed on a daily basis, as it is stored in Amazon S3 and can be retrieved on demand.

It offers flexible data protection and recovery options, as it allows taking point-in-time copies of the volumes using AWS Backup, which are stored in AWS as Amazon EBS snapshots.

Therefore, the solutions architect should recommend deploying an AWS Storage Gateway volume gateway with cached volumes with an Amazon S3 bucket as the target storage and migrating the data to the Storage Gateway appliance.

To maintain predictable database performance and ensure that the Lambda invocations do not overload the database with too many connections, a solutions architect should point the client driver at an RDS proxy endpoint and deploy the Lambda functions inside a VPC. An RDS proxy is a fully managed database proxy that allows applications to share connections to a database, improving database availability and scalability. By using an RDS proxy, the Lambda functions can reuse existing connections, rather than creating new ones for every invocation, reducing the connection overhead and latency. Deploying the Lambda functions inside a VPC allows them to access the private RDS DB instance securely and efficiently, without exposing it to the public internet. References:

Using Amazon RDS Proxy with AWS Lambda

Configuring a Lambda function to access resources in a VPC

AWS Site-to-Site VPN: This provides a secure and encrypted connection between an on-premises environment and AWS. It is a cost-effective solution suitable for low bandwidth and small traffic needs.

Quick Setup:

Site-to-Site VPN can be quickly set up by configuring a virtual private gateway on the AWS side and a customer gateway on the on-premises side.

It uses standard IPsec protocol to establish the VPN tunnel.

Cost-Effectiveness: Compared to AWS Direct Connect, which requires dedicated physical connections and higher setup costs, a Site-to-Site VPN is less expensive and easier to implement for smaller traffic requirements.

To meet the requirements of the web application in the most secure manner, the company should create a Route 53 Resolver outbound endpoint, create a resolver rule, and associate the resolver rule with the VPC. This solution will allow the application to use private DNS records to communicate with the on-premises services from a VPC. Route 53 Resolver is a service that enables DNS resolution between on-premises networks and AWS VPCs. An outbound endpoint is a set of IP addresses that Resolver uses to forward DNS queries from a VPC to resolvers on an on-premises network. A resolver rule is a rule that specifies the domain names for which Resolver forwards DNS queries to the IP addresses that you specify in the rule. By creating an outbound endpoint and a resolver rule, and associating them with the VPC, the company can securely resolve DNS queries for the on-premises services using private DNS records12.

The other options are not correct because they do not meet the requirements or are not secure. Creating a Route 53 Resolver inbound endpoint, creating a resolver rule, and associating the resolver rule with the VPC is not correct because this solution will allow DNS queries from on-premises networks to access resources in a VPC, not vice versa. An inbound endpoint is a set of IP addresses that Resolver uses to receive DNS queries from resolvers on an on-premises network1. Creating a Route 53 private hosted zone and associating it with the VPC is not correct because this solution will only allow DNS resolution for resources within the VPC or other VPCs that are associated with the same hosted zone. A private hosted zone is a container for DNS records that are only accessible from one or more VPCs3. Creating a Route 53 public hosted zone and creating a record for each service to allow service communication is not correct because this solution will expose the on-premises services to the public internet, which is not secure. A public hosted zone is a container for DNS records that are accessible from anywhere on the internet3.

The AWS Fargate launch type is a serverless way to run containers on Amazon ECS, without having to manage any underlying infrastructure. You only pay for the resources required to run your containers,and AWS handles the provisioning, scaling, and security of the cluster. Amazon EFS is a fully managed, elastic, and scalable file system that can be mounted to multiple containers, and provides high availability and durability. By using AWS Fargate and Amazon EFS, you can run your Docker container image with 50 GB of storage available for temporary files, with the least operational overhead. This solution meets the requirements of the question.

Amazon Elastic File System (EFS) is the ideal solution for migrating legacy applications that require NFS (Network File System) communication. EFS provides fully managed, scalable NFS storage in the cloud, and it supports the standard NFS protocols, allowing the legacy application to continue using NFS without modification after migration to AWS.

Key AWS features:

NFS Support: EFS natively supports the NFSv4 protocol, which makes it the best solution for workloads that rely on NFS communication.

Scalability and Availability: EFS automatically scales as application demands grow, making it a highly available and reliable storage solution.

AWS Documentation: According to AWS’s best practices for file storage, EFS is recommended for any workloads requiring NFS support in a cloud environment​​.

This solution will meet the requirements of high availability and eventual consistency with the least operational overhead. By modifying the Auto Scaling group to use EC2 instances acrossthree Availability Zones, the web application can handle the increase in traffic and tolerate the failure of one or two Availability Zones. By migrating the embedded NoSQL database to Amazon DynamoDB, the company can benefit from a fully managed, scalable, and reliable NoSQL database service that supports eventual consistency. AWS Database Migration Service (AWS DMS) is a cloud service that makes it easy to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. AWS DMS can migrate the embedded NoSQL database to Amazon DynamoDB with minimal downtime and zero data loss.

This solution meets the requirements because it allows the monitoring account to query and visualize observability data across the accounts by using CloudWatch. CloudWatch cross-account observability is a feature that enables a central monitoring account to view and interact with observability data shared by other accounts. To enable cross-account observability, the monitoring account needs to configure the types of data to be shared (metrics, logs, and traces) and the source accounts to be linked. The source accounts can be specified by account IDs, organization IDs, or organization paths. To share the data with the monitoring account, the source accounts need to deploy an AWS CloudFormation template provided by the monitoring account. This template creates an observability link resource that represents the link between the source account and the monitoring account. The template also creates a sink resource that represents an attachment point in the monitoring account. The source accounts can share theirobservability data with the sink in the monitoring account. The monitoring account can then use the CloudWatch console, API, or CLI to search, analyze, and correlate the observability data across the accounts. References: CloudWatch cross-account observability, Setting up CloudWatch cross-account observability, [Observability Access Manager API Reference]

This solution meets the requirements because it allows the company to use a single Direct Connect connection to connect to multiple VPCs in different Regions using a Direct Connect gateway. A Direct Connect gateway is a globally available resource that enables you to connect your on-premises network to VPCs in any AWS Region, except the AWS China Regions. You can associate a Direct Connect gateway with a transit gateway or a virtual private gateway in each Region. By routing traffic from the virtual private gateways of the VPCs to the Direct Connect gateway, you can enable inter-Region and on-premises connectivity for your VPCs. This solution is scalable because you can add more VPCs in different Regions to the Direct Connect gateway without creating additional connections. This solution also reduces operational overhead because you do not need to manage multiple VPN appliances, VPN connections, or VPC peering connections.

https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

This solution meets the requirements of measuring the effectiveness of marketing campaigns by performing batch processing on csv files of sales data and storing the results in an Amazon S3 bucket once every hour. An AWS duo ETL process can use services such as AWS Glue or AWS Data Pipeline to extract data from S3, transform it into a more efficient format such as Apache Parquet, and load it back into S3. Apache Parquet is a columnar storage format that can improve the query performance and reliability of Athena by reducing the amount of data scanned, improving compression ratio, and enabling predicate pushdown.

Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, SAP, Zendesk, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks.https://aws.amazon.com/appflow/

In Static Websites, Web pages are returned by the server which are prebuilt.

They use simple languages such as HTML, CSS, or JavaScript.

There is no processing of content on the server (according to the user) in Static Websites. Web pages are returned by the server with no change therefore, static Websites are fast.

There is no interaction with databases.

Also, they are less costly as the host does not need to support server-side processing with different languages.

============

In Dynamic Websites, Web pages are returned by the server which are processed during runtime means they are not prebuilt web pages but they are built during runtime according to the user’s demand.

These use server-side scripting languages such as PHP, Node.js, ASP.NET and many more supported by the server.

So, they are slower than static websites but updates and interaction with databases are possible.

The most suitable solution for the company’s requirements is to enable Default Host Configuration Management in Systems Manager to manage the EC2 instances. This solution will allow the company to patch the EC2 instances without disrupting the running applications and without manually creating or modifying IAM roles or users.

Default Host Configuration Management is a feature of AWS Systems Manager that enables Systems Manager to manage EC2 instances automatically as managed instances. A managed instance is an EC2 instance that is configured for use with Systems Manager. The benefits of managing instances with Systems Manager include the following:

Connect to EC2 instances securely using Session Manager.

Perform automated patch scans using Patch Manager.

View detailed information about instances using Systems Manager Inventory.

Track and manage instances using Fleet Manager.

Keep SSM Agent up to date automatically.

Default Host Configuration Management makes it possible to manage EC2 instances without having to manually create an IAM instance profile. Instead, Default Host Configuration Management creates and applies a default IAM role to ensure that Systems Manager has permissions to manage all instances in the Region and account where it is activated.If thepermissions provided are not sufficient for the use case, the default IAM role can be modified or replaced with a custom role1.

The other options are not correct because they either have more operational overhead or do not meet the requirements. Creating a new IAM role, attaching the AmazonSSMManagedInstanceCore policy to the new IAM role, and attaching the new IAM role and the existing IAM role to the EC2 instances is not correct because this solution requires manual creation and management of IAM roles, which adds complexity and cost to the solution.The AmazonSSMManagedInstanceCore policy is a managed policy that grants permissions for Systems Manager core functionality2. Creating an IAM user, attaching the AmazonSSMManagedInstanceCore policy to the IAM user, and configuring Systems Manager to use the IAM user to manage the EC2 instances is not correct because this solution requires manual creation and management of IAM users, which adds complexity and cost to the solution.An IAM user is an identity within an AWS account that has specific permissions for a single person or application3. Removing the existing policies from the existing IAM role and adding the AmazonSSMManagedInstanceCore policy to the existing IAM role is not correct because this solution may disrupt the running applications that rely on the existing policies for accessing RDS databases.An IAM role is an identity within an AWS account that has specific permissions for a service or entity4.

AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of the AWS Cloud. Snowball addresses common challenges with large-scale data transfers including high network costs, long transfer times, and security concerns. AWS Snowball can transfer up to 80 TB of data per device, and multiple devices can be used in parallel to meet the migration deadline. AWS Snowball is more cost-effective than AWS Snowmobile, which is designed for exabyte-scale data transfers, or Amazon S3 Transfer Acceleration, which is optimized for fast transfers over long distances. Amazon S3 VPC endpoint does not increase the upload speed, but only provides a secure and private connection between the VPC and S3. References: AWS Snowball, AWS Snowmobile, Amazon S3 Transfer Acceleration, Amazon S3 VPC endpoint

https://www.learnaws.org/2020/12/13/aws-rds-proxy-deep-dive/

RDS proxy can improve application availability in such a situation by waiting for the new database instance to be functional and maintaining any requests received from the application during this time. The end result is that the application is more resilient to issues with the underlying database.

This will enable solution to hold data till the time DB comes back to normal. RDS proxy is to optimally utilize the connection between Lambda and DB. Lambda can open multipleconnection concurrently which can be taxing on DB compute resources, hence RDS proxy was introduced to manage and leverage these connections efficiently.

To achieve high availability with minimum downtime and minimum loss of data, the Auto Scaling group should be configured to use multiple Availability Zones to ensure that there is no single point of failure. The database should be configured as Multi-AZ to enable automatic failover in case of an outage in the primary Availability Zone. Additionally, an Amazon RDSProxy instance can be used to improve the scalability and availability of the database by reducing connection failures and improving failover times.

To provide the product manager access to the Amazon CloudWatch dashboard while following the principle of least privilege, a solution architect should create an IAM user specifically for the product manager and attach the CloudWatch Read Only Access managed policy to the user. This policy allows the user to view the dashboard without being able tomake any changes to it. The solution architect should then share the new login credential with the product manager and provide them with the browser URL of the correct dashboard.

Creating an Amazon Simple Queue Service (SQS) queue and configuring the S3 bucket to send a notification to the SQS queue when an image is uploaded to the S3 bucket will ensure that the Lambda function is triggered in a stateless and durable manner.

Configuring the Lambda function to use the SQS queue as the invocation source, and deleting the message in the queue after it is successfully processed will ensure that the Lambda function processes the image in a stateless and durable manner.

Amazon SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating-message oriented middleware, and empowers developers to focus on differentiating work. When new images are uploaded to the S3 bucket, SQS will trigger the Lambda function to process the image and compress it. Once the image is processed, the SQS message is deleted, ensuring that the Lambda function is stateless and durable.

EFS is a standard file system, it scales automatically and is highly available.

https://aws.amazon.com/ebs/features/

"Provisioned IOPS volumes are backed by solid-state drives (SSDs) and are the highest performance EBS volumes designed for your critical, I/O intensive database applications.

These volumes are ideal for both IOPS-intensive and throughput-intensive workloads that require extremely low latency."

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html

S3 One Zone-IA is a storage class that is designed for data that is accessed less frequently, but requires rapid access when needed. Unlike other S3 Storage Classes which store data in a minimum of three Availability Zones (AZs), S3 One Zone-IA stores data in a single AZ and costs 20% less than S3 Standard-IA. This storage class meets the requirements of the company because it provides a low-cost solution for the secondary copy of its on-premises dataset that would rarely need to be accessed. The other storage classes are either more expensive or not suitable for infrequently accessed data.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html

Fromhttps://docs.aws.amazon.com/kms/latest/developerguide/custom-key-store-overview.html

For most users, the default AWS KMS key store, which is protected by FIPS 140-2 validated cryptographic modules, fulfills their security requirements. There is no need to add an extra layer of maintenance responsibility or a dependency on an additional service. However, you might consider creating a custom key store if your organization has any of the following requirements: Key material cannot be stored in a shared environment. Key material must be subject to a secondary, independent audit path. The HSMs that generate and store key material must be certified at FIPS 140-2 Level 3.https://docs.aws.amazon.com/kms/latest/developerguide/custom-key-store-overview.html

https://docs.aws.amazon.com/kms/latest/developerguide/multi-region-keys-overview.html

https://docs.aws.amazon.com/systems-manager/latest/userguide/setup-launch-managed-instance.html

https://aws.amazon.com/getting-started/hands-on/build-serverless-web-app-lambda-apigateway-s3-dynamodb-cognito/module-4/

Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito. This example showed similar setup as question: Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito

https://aws.amazon.com/blogs/security/how-to-replicate-secrets-aws-secrets-manager-multiple-regions/

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-reservations.html

Reserve instances: You will have to pay for the whole term (1 year or 3years) which is not cost effective

To maximize resiliency and scalability, the best solution is to use an Amazon SQS queue as a destination for the jobs. This decouples the primary server from the compute nodes, allowing them to scale independently. This also helps to prevent job loss in the event of a failure. Using an Auto Scaling group of Amazon EC2 instances for the compute nodes allows for automatic scaling based on the workload. In this case, it's recommended to configure the Auto Scaling group based on the size of the Amazon SQS queue, which is a better indicator of the actual workload than the load on the primary server or compute nodes. This approach ensures that the application can handle variable workloads, while also minimizing costs by automatically scaling up or down the compute nodes as needed.

Static content can be cached at Cloud front Edge locations from S3 and dynamic content EC2 behind the ALB whose performance can be improved by Global Accelerator whose one endpoint is ALB and other Cloud front. So with regards to custom domain name endpoint is web application is R53 alias records for the custom domain point to web applicationhttps://aws.amazon.com/blogs/networking-and-content-delivery/improving-availability-and-performance-for-application-load-balancers-using-one-click-integration-with-aws-global-accelerator/

The allowed block size is between a /28 netmask and /16 netmask. The CIDR block must not overlap with any existing CIDR block that's associated with the VPC.https://docs.aws.amazon.com/vpc/latest/userguide/configure-your-vpc.html

To ensure all Amazon EC2 instances, Amazon RDS DB instances, and Amazon Redshift clusters are configured with tags, a solutions architect should use AWS Config rules to define and detect resources that are not properly tagged. AWS Config rules are a set of customizable rules that AWS Config uses to evaluate AWS resource configurations for compliance with best practices and company policies. Using AWS Config rules can minimize the effort of configuring and operating this check because it automates the process of identifying non-compliant resources and notifying the responsible teams.

https://aws.amazon.com/shield/faqs/

Application availability: NLB cannot assure the availability of the application. This is because it bases its decisions solely on network and TCP-layer variables and has no awareness of the application at all. Generally, NLB determines availability based on the ability of a server to respond to ICMP ping or to correctly complete the three-way TCP handshake. ALB goes much deeper and is capable of determining availability based on not only a successful HTTP GET of a particular page but also the verification that the content is as was expected based on the input parameters.

To meet the requirements of detecting and alerting the administrators when PII is shared and automating remediation with the least development effort, the best approach would be to use Amazon S3 bucket as a secure transfer point and scan the objects in the bucket with Amazon Macie. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data stored in Amazon S3. It can be used to classify sensitive data, monitor access to sensitive data, and automate remediation actions.

In this scenario, after uploading the files to the Amazon S3 bucket, the objects can be scanned for PII by Amazon Macie, and if it detects any PII, it can trigger an Amazon Simple Notification Service (SNS) notification to alert the administrators to remove the objects containing PII. This approach requires the least development effort, as Amazon Macie already has pre-built data classification rules that can detect PII in various formats.

Hence, option B is the correct answer.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/privatelink-interface-endpoints.html#types-of-vpc-endpoints-for-s3

https://docs.aws.amazon.com/vpc/latest/userguide/vpc-endpoints-s3.html

These two steps will meet the requirements with the fewest changes to the application because they will enable the company to replicate the data to the new S3 buckets and minimize latency for both video streaming and uploads. One-way replication from the us-east-2 S3 bucket to the other two S3 buckets will ensure that the data is synchronized across all three regions. The company can use S3 Cross-Region Replication (CRR) to automatically copy objects across buckets in different AWS Regions. CRR can help the company achieve lower latency and compliance requirements by keeping copies of their data in different regions. Creating an S3 Multi-Region Access Point and modifying the application to use its ARN will allow the company to access the data through a single global endpoint. An S3 Multi-Region Access Point is a globally unique name that can be used to access objects stored in S3 buckets across multiple regions. It automatically routes requests to the closest S3 bucket with the lowest latency. By using an S3 Multi-Region Access Point, the company can simplify the application architecture and improve the performance and reliability of the application.

The least outstanding requests (LOR) algorithm is a load balancing algorithm that distributes incoming requests to the target with the fewest outstanding requests. This helps to avoid overloading any single target and improves the overall performance and availability of the web application. The LOR algorithmcan use the RequestCount and TargetResponseTime CloudWatch metrics to determine the number of outstanding requests and the response time of each target. These metrics measure the number of requests processed by each target and the time elapsed after the request leaves the load balancer until a response from the target is received by the load balancer, respectively. By using these metrics, the LOR algorithm can route new requests to the targets that are less busy and more responsive, and avoid sending requests to the targets that are already overloaded or slow. This solution meets the requirements of the company.

https://aws.amazon.com/blogs/security/control-access-to-aws-resources-by-using-the-aws-organization-of-iam-principals/

"For archive data that needs immediate access, such as medical images, news media assets, or genomics data, choose the S3 Glacier Instant Retrieval storage class, an archive storage class that delivers the lowest cost storage with milliseconds retrieval. For archive data that does not require immediate access but needs the flexibility to retrieve large sets of data at no cost, such as backup or disaster recovery use cases, choose S3 Glacier Flexible Retrieval (formerly S3 Glacier), with retrieval in minutes or free bulk retrievals in 5-12 hours."https://aws.amazon.com/about-aws/whats-new/2021/11/amazon-s3-glacier-instant-retrieval-storage-class/

To reduce the cost of running the tests without reducing the compute and memory attributes of the Amazon RDS for MySQL DB instance, the development team can stop the instance when tests are completed and restart it when required. Stopping the DB instance when not in use can help save costs because customers are only charged for storage while the DB instance is stopped. During this time, automated backups and automated DB instance maintenance are suspended.When the instance is restarted, it retains the same configurations, security groups, and DB parameter groups as when it was stopped.

https://docs.aws.amazon.com/filegateway/latest/filefsxw/what-is-file-fsxw.html

To meet the requirements of the company to have access to both AWS and on-premises file storage with minimum latency, a hybrid cloud architecture can be used. One solution is to deploy and configure Amazon FSx for Windows File Server on AWS, which provides fully managed Windows file servers. The on-premises file data can be moved to the FSx File Gateway, which can act as a bridge between on-premises and AWS file storage. The cloudworkloads can be configured to use FSx for Windows File Server on AWS, while the on-premises workloads can be configured to use the FSx File Gateway. This solution minimizes operational overhead and requires no significant changes to the existing file access patterns. The connectivity between on-premises and AWS can be established using an AWS Site-to-Site VPN connection.

To ensure that Amazon S3 buckets do not have unauthorized configuration changes, a solutions architect should turn on AWS Config with the appropriate rules. AWS Config is a service that allows users to audit and assess their AWS resource configurations for compliance with industry standards and internal policies. It provides a detailed view of the resources and their configurations, including information on how the resources are related to each other. By turning on AWS Config with the appropriate rules, users can identify and remediate unauthorized configuration changes to their Amazon S3 buckets.

Understanding the Requirement: The application needs to write to multiple block storage volumes in multiple EC2 Nitro-based instances simultaneously to achieve higher availability.

Analysis of Options:

General Purpose SSD (gp3) with Multi-Attach: Supports Multi-Attach but does not provide the highest performance required for critical applications.

Throughput Optimized HDD (st1) with Multi-Attach: Not suitable for applications requiring high performance and low latency.

Provisioned IOPS SSD (io2) with Multi-Attach: Provides high performance and durability, suitable for applications requiring simultaneous writes and high availability.

General Purpose SSD (gp2) with Multi-Attach: Similar to gp3 but with less flexibility and performance.

Best Solution:

Provisioned IOPS SSD (io2) with Multi-Attach: This solution ensures the highest performance and availability for the application by allowing multiple EC2 instances to attach to and write to the same EBS volume simultaneously.

The correct solution is to provision a separate AWS KMS key for each customer and encrypt the data server-side. This way, the company can use the S3 encryption feature to protect the data at rest and delegate the control of the encryption keys to the customers. The customers can then use their own IAM roles to access and decrypt their data. The company employees will not be able to access the data because they are not authorized by the KMS key policies. The other options are incorrect because:

Option A and D are using ACM certificates to encrypt the data client-side. This is not a recommended practice for S3 encryption because it adds complexity and overhead to the encryption process. Moreover, the company will have to manage the certificates and their policies for each customer, which is not scalable and secure.

Option B is using a separate KMS key for each customer, but it is using the S3 bucket policy to control the decryption access. This is not a secure solution because the bucket policy applies to the entire bucket, not to individual objects. Therefore, the customers will be able to access and decrypt each other’s data if they have the permission to list the bucket contents. The bucket policy also overrides the KMS key policy, which means the company employees can access the data if they have the permission to use the KMS key.

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html#:~:text=Don%27t%20rely%20on%20API%20keys%20as%20your%20only%20means%20of%20authentication%20and%20authorization%20for%20your%20APIs

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html

By purchasing a Compute Savings Plan, the company can save on the costs of running both EC2 instances and Lambda functions. The Lambda functions can be connected to the private subnet that contains the EC2 instances through a VPC endpoint for AWS services or a VPC peering connection. This provides direct network access to the EC2 instances while keeping the traffic within the private network, which helps to minimize network latency. Optimizing the Lambda functions’ duration, memory usage, number of invocations, and amount of data transferred can help to further minimize costs and improve performance. Additionally, using a private subnet helps to ensure that the EC2 instances are not directly accessible from the public internet, which is a security best practice.

Since the application has no local data on instances, AMIs alone can meet the RPO by restoring instances from the most recent AMI backup. When combined with automated RDS backups for the database, this provides a complete backup solution for this environment. The other options involving EBS snapshots would be unnecessary given the stateless nature of the instances. AMIs provide all the backup needed for the app tier. This uses native, automated AWS backup features that require minimal ongoing management: - AMI automated backups provide point-in-time recovery for the stateless app tier. - RDS automated backups provide point-in-time recovery for the database.

https://aws.amazon.com/blogs/security/how-to-use-aws-secrets-manager-rotate-credentials-amazon-rds-database-types-oracle/

https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

Creating a peering connection between the two VPCs and configuring an inbound rule for the ElastiCache cluster's security group to allow inbound connection from the application's security group is the most cost-effective solution. Peering connections are free and you only incur the cost of configuring the security group rules. The Transit VPC solution requires additional VPCs and associated resources, which would incur additional costs.

Before Testing | AWS Certification Information and Policies | AWS

https://aws.amazon.com/certification/policies/before-testing/

This solution meets the following requirements:

It is private and secure, as it allows the EC2 instances to access the S3 bucket without using the public internet. A VPC endpoint is a gateway that enables you to create a private connection between your VPC and another AWS service, such as S3, within the same Region. A VPC endpoint for S3 provides secure and direct access to S3 buckets and objects using private IP addresses from your VPC. You can also use VPC endpoint policies and S3 bucket policies to control the access to the S3 resources based on the endpoint, the IAM user, the IAM role, or the source IP address.

It is simple and scalable, as it does not require any additional AWS services, gateways, or NAT devices. A VPC endpoint for S3 is a fully managed service that scales automatically with the network traffic. You can create a VPC endpoint for S3 with a few clicks in the VPC console or with a simple API call. You can also use the same VPC endpoint to access multiple S3 buckets in the same Region.

Enabling Amazon Elastic Block Store (Amazon EBS) fast snapshot restore on a snapshot allows you to quickly create a new Amazon Machine Image (AMI) from a snapshot, which can help reduce the initialization latency when provisioning new instances. Once the AMI is provisioned, you can replace the AMI in the Auto Scaling group with the new AMI. This will ensure that new instances are launched from the updated AMI and are able to meet the increased demand quickly.

This option is the most efficient because it uses a gateway VPC endpoint for Amazon S3, which provides reliable connectivity to Amazon S3 without requiring an internet gateway or a NAT device for the VPC1. A gateway VPC endpoint routes traffic from the VPC to Amazon S3 using a prefix list for the service and does not leave the AWS network2. This meets the requirement of not traversing the internet. Option A is less efficient because it uses a private hosted zone by using Amazon Route 53, which is a DNS service that allows you to create custom domain names for your resources within your VPC3. However, this does not provide connectivity to Amazon S3 without an internet gateway or a NAT device. Option C is less efficient because it uses a NAT gateway to access the S3 bucket, which is a highly available, managed Network Address Translation (NAT) service that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating a connection with those instances4. However, this does not meet the requirement of not traversing the internet. Option D is less efficient because it uses an AWS Site-to-Site VPN connection between the VPC and the S3 bucket, which is a secure and encrypted network connection between your on-premises network and your VPC. However, this does not meet the requirement of not traversing the internet.

https://aws.amazon.com/premiumsupport/knowledge-center/waf-block-common-attacks/#:~:text=To%20protect%20your%20applications%20against,%2C%20query%20string%2C%20or%20URI. ----------------------------------------------------------------------------------------------------------------------- Protect against SQL injection and cross-site scripting To protect your applications against SQL injection and cross-site scripting (XSS) attacks, use the built-in SQL injection and cross-site scripting engines. Remember that attacks can be performed ondifferent parts of the HTTP request, such as the HTTP header, query string, or URI. Configure the AWS WAF rules to inspect different parts of the HTTP request against the built-in mitigation engines.

https://aws.amazon.com/vpc/faqs/#:~:text=Can%20a%20subnet%20span%20Availability,within%20a%20single%20Availability%20Zone.

Option A is the most appropriate solution for reducing replication lag without significant changes to the application code and minimizing ongoing operational overhead. Migrating the database to Amazon Aurora MySQL allows for improved replication performance and higher scalability compared to Amazon RDS for MySQL. Aurora Replicas provide faster replication, reducing the replication lag, and Aurora Auto Scaling ensures that there are enough Aurora Replicas to handle the incoming traffic. Additionally, Aurora MySQL native functions can replace the stored procedures, reducing the load on the database and improving performance.

https://docs.aws.amazon.com/kinesisanalytics/latest/dev/what-is.html

AWS Snowball is a secure data transport solution that accelerates moving large amounts of data into and out of the AWS cloud. It can move up to 80 TB of data at a time, and provides a network bandwidth of up to 50 Mbps, so it is well-suited for the task. Additionally, it is secure and easy to use, making it the ideal solution for this migration.

the best solution is to implement Amazon ElastiCache to cache the large datasets, which will store the frequently accessed data in memory, allowing for faster retrieval times. This can help to alleviate the frequent calls to the database, reduce latency, and improve the overall performance of the backend tier.

The best solution to meet these requirements with the least amount of operational overhead is to enable Amazon DynamoDB Streams on the table and use triggers to write to a single AmazonSimple Notification Service (Amazon SNS) topic to which the teams can subscribe. This solution requires minimal configuration and infrastructure setup, and Amazon DynamoDB Streams provide a low-latency way to capture changes to the DynamoDB table. The triggers automatically capture the changes and publish them to the SNS topic, which notifies the internal teams.

Windows file server is on-premise and we need something to replicate the data to the cloud, the only option we have is AWS FSx for Windows File Server. Also, since the information is confidential and sensitive, we also want to make sure that the appropriate users have access to it in a secure manner.https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

The best option is to use AWS CloudTrail to find the desired information. AWS CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of AWS account activities. CloudTrail can be used to log all changes made to resources in an AWS account, including changes made by IAM users, EC2 instances, AWS management console, andother AWS services. By using CloudTrail, the solutions architect can identify the IAM user who made the configuration changes to the security group rules.

UESTION NO: 549

A company has a three-tier environment on AWS that ingests sensor data from its users' devices The traffic flows through a Network Load Balancer (NIB) then to Amazon EC2 instances for the web tier and finally to EC2 instances for the application tier that makes database calls

What should a solutions architect do to improve the security of data in transit to the web tier?

A. Configure a TLS listener and add the server certificate on the NLB

B. Configure AWS Shield Advanced and enable AWS WAF on the NLB

C. Change the load balancer to an Application Load Balancer and attach AWS WAF to it

D. Encrypt the Amazon Elastic Block Store (Amazon EBS) volume on the EC2 instances using AWS Key Management Service (AWS KMS)

Answer: A

A: How do you protect your data in transit?

Best Practices:

Implement secure key and certificate management: Store encryption keys and certificates securely and rotate them at appropriate time intervals while applying strict access control; for example, by using a certificate management service, such as AWS Certificate Manager (ACM).

Enforce encryption in transit: Enforce your defined encryption requirements based on appropriate standards and recommendations to help you meet your organizational, legal, and compliance requirements.

Automate detection of unintended data access: Use tools such as GuardDuty to automatically detect attempts to move data outside of defined boundaries based on data classification level, for example, to detect a trojan that is copying data to an unknown or untrusted network using the DNS protocol.

Authenticate network communications: Verify the identity of communications by using protocols that support authentication, such as Transport Layer Security (TLS) or IPsec.

https://wa.aws.amazon.com/wat.question.SEC_9.en.html

https://aws.amazon.com/step-functions/#:~:text=AWS%20Step%20Functions%20is%20a,machine%20learning%20(ML)%20pipelines.

"A common use case for AWS Step Functions is a task that requires human intervention (for example, an approval process). Step Functions makes it easy to coordinate the componentsof distributed applications as a series of steps in a visual workflow called a state machine. You can quickly build and run state machines to execute the steps of your application in a reliable and scalable fashion. (https://aws.amazon.com/pt/blogs/compute/implementing-serverless-manual-approval-steps-in-aws-step-functions-and-amazon-api-gateway/) "

Migrate MySQL database to an Amazon Aurora global database is the best solution because it requires minimal operational overhead. Aurora is a managed service that provides automatic failover, so standby instances do not need to be manually configured. The primary DB cluster can be hosted in the primary Region, and the secondary DB cluster can be hosted in the DR Region. This approach ensures that the data is always available and up-to-date in multiple Regions, without requiring significant manual intervention.

This option is the most secure because it follows the principle of least privilege and grants only the necessary permissions to the Lambda function without exposing any credentials in the code. The IAM role can be configured as the Lambda function’sexecution role and the IAM policy can specify the S3 bucket ARN and the s3:GetObject action12. Option A is less secure because it grants read access to any principal that has access to the S3 bucket, which could be more than the Lambda function. Option C is less secure because it embeds credentials in the code, which could be compromised or exposed. Option D is less secure because it grants read access to all S3 buckets in the account, which could be more than what the Lambda function needs.

https://aws.amazon.com/blogs/networking-and-content-delivery/adding-http-security-headers-using-lambdaedge-and-amazon-cloudfront/

Amazon CloudFront is a global content delivery network (CDN) service that can securely deliver web content, videos, and APIs at scale. It integrates with Cognito for authentication and with Lambda@Edge for authorization, making it an ideal choice for serving web content globally. Lambda@Edge is a service that lets you run AWS Lambda functions globally closer to users, providing lower latency and faster response times. It can also handle authorization logic at the edge to secure content in CloudFront. For this scenario, Lambda@Edge can provide authorization for the web application while leveraging the low-latency benefit of running at the edge.

This option is the most efficient because it uses Cost Explorer, which is a tool that allowsyou to visualize, understand, and manage your AWS costs and usage over time1. You can create a report in CostExplorer that lists AWS billed items by user, using the user name tag as a filter2. You can then download the report as a CSV file and use it for budget planning. Option A is less efficient because it uses Amazon Athena, which is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL3. You would need to set up an Athena table that points to your AWS Cost and Usage Report data in S3, and then run a query to generate the report. This would incur additional costs and complexity. Option C is less efficient because it uses the billing dashboard, which provides a high-level summary of your AWS costsand usage. You can access the bill details from the billing dashboard and download them via bill, but this would not list the billed items by user. You would need to use tags to group your costs by user name, which would require additional steps. Option D is less efficient because it uses AWS Budgets, which is a tool that allows you to plan your service usage, service costs, and instance reservations. You can modify a cost budget in AWS Budgets to alert with Amazon Simple Email Service (Amazon SES), but this would not generate a report of AWS billed items by user. This would only notify you when your actual or forecasted costs exceed or are expected to exceed your budgeted amount.

Amazon EC2 Auto Scaling offers the ability to add lifecycle hooks to your Auto Scaling groups. These hooks let you create solutions that are aware of events in the Auto Scaling instance lifecycle, and then perform a custom action on instances when the corresponding lifecycle event occurs. (https://docs.aws.amazon.com/autoscaling/ec2/userguide/lifecycle-hooks.html)

Configure provisioned concurrency for the Lambda function that handles the requests. Provisioned concurrency allows you to set the amount of compute resources that are available to the Lambda function, so that it can handle more requests at once and reduce latency. Caching the results of the queries in Amazon S3 could also help to reduce latency, but it would not be as effective as setting up provisioned concurrency. Increasing the size of the database would not help to reduce latency, as this would not increase the number of connections the Lambda function could establish, and establishing a direct connection between the frontend application and the database would bypass the API, which would not be the best solution either.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html

AWS Lambda FAQs

https://aws.amazon.com/lambda/faqs/

The data retention period of a Kinesis data stream is the time period from when a record is added to when it is no longer accessible1. Thedefault retention period for a Kinesis data stream is 24 hours, which can be extended up to 8760 hours (365 days)1. The dataretention period can be updated by using the AWS Management Console, the AWS CLI, or the Kinesis Data Streams API1.

To meet the requirements of the scenario, the solutions architect should update the Kinesis Data Streams default settings by modifying the data retention period. The solutions architect should increase the retention period to a value that is greater than or equal to thefrequency of consuming the data and writing itto S32. This way, the company can ensure that S3 receives all the data that the application sends to Kinesis Data Streams.

Amazon FSx for Windows File Server (Amazon FSx) is a fully managed, highly available, and scalable file storage solution built on Windows Server that uses the Server Message Block (SMB) protocol. It allows for Microsoft Active Directory integration, data deduplication, and fully managed backups, among other critical enterprise features.https://aws.amazon.com/blogs/storage/accessing-smb-file-shares-remotely-with-amazon-fsx-for-windows-file-server/

Create an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system on all web servers. To meet the requirements of providing a shared file store for Linux-based web servers without making changes to the application, using an Amazon EFS file system is the best solution. Amazon EFS is a managed NFS file system service that provides shared access to files across multiple Linux-based instances, which makes it suitable for this use case. Amazon S3 is not ideal for this scenario since it is an object storage service and not a file system, and it requires additional tools or libraries to mount the S3 bucket as a filesystem. Amazon CloudFront can be used to improve content delivery performance but is not necessary for this requirement. Additionally, Amazon EBS volumes can only be mounted to one instance at a time, so it is not suitable for sharing files across multiple instances.

Amazon DocumentDB (with MongoDB compatibility) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

https://medium.com/awesome-cloud/aws-difference-between-multi-az-and-read-replicas-in-amazon-rds-60fe848ef53a

Amazon Transcribe now supports speaker labeling for streaming transcription. Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for you to convert speech-to-text. In live audio transcription, each stream of audio may contain multiplespeakers. Now you can conveniently turn on the ability to label speakers, thus helping to identify who is saying what in the output transcript.https://aws.amazon.com/about-aws/whats-new/2020/08/amazon-transcribe-supports-speaker-labeling-streaming-transcription/

• Amazon S3 for large-scale, durable, and inexpensive storage of the video content. S3 storage costs are significantly lower than EFS. • Amazon EBS only temporarily during processing. By mounting an EBS volume only when a video needs to be processed, and unmounting it after, the time the content spends on the higher-cost EBS storage is minimized. • The EBS volume can be sized to match the workload needs for active processing, keeping costs lower. The volume does not need to store the entire video library long-term.

AWS Application Migration Service (AWS MGN) is the recommended tool for a lift-and-shift strategy, especially for time-sensitive migrations. It automates the replication of on-premises VMs to AWS, minimizing the effort required for migration and testing.

Key steps:

Replication with AWS MGN: The AWS Replication Agent is installed on the VMs to continuously replicate data to AWS, allowing you to manage migration easily.

Testing and Cutover: Initial replication allows for testing in AWS before performing the final cutover, ensuring that the migration process is smooth and data integrity is maintained.

AWS Documentation: AWS MGN is recommended for migrating virtual machines to the cloud with minimal downtime and disruption​​.

Amazon Macieis purpose-built for detecting PII in S3.

Option Auses EventBridge to filter SensitiveData findings and notify via SNS, meeting the requirements.

Options B and Dinvolve GuardDuty, which is not designed for PII detection.

Option Cuses SQS, which is less suitable for immediate notifications.

This option is the most efficient because it uses AWS DataSync, which is a secure, online service that automates and accelerates moving data between on-premises and AWS Storage services1. It also uses DataSync to transfer large amounts of data back and forth between NFS file systems in the two Regions on a periodic basis, which simplifies and speeds up the data transfer process with minimal operational overhead. This solution meets the requirement of transferring large amounts of data back and forth between NFS file systems in the two Regions on a periodic basis with the least operational overhead. Option B is less efficient because it uses AWS Snowball devices, which are physical devices that let you transfer large amounts of data into and out of AWS2. However, this does not provide a periodic data transfer solution, as it requires manual handling and shipping of the devices. Option C is less efficient because it sets up an SFTP server on Amazon EC2, which is a way to provide secure file transfer protocol (SFTP) access to files stored in Amazon S33. However, this does not provide a periodic data transfer solution, as it requires manual initiation and monitoring of the file transfers. Option D is less efficient because it uses AWS Database Migration Service (AWS DMS), which is a service that helps you migrate databases to AWS quickly and securely. However, this does not provide a data transfer solution for NFS file systems, as it only supports relational databases and non-relational data stores.

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

A) Enable AWS CloudTrail and use it for auditing. AWS CloudTrail provides a record of API calls and can be used to audit changes made to EC2 instances and security groups. By analyzing CloudTrail logs, the solutions architect can track who provisioned oversized instances ormodified security groups without proper approval. D) Enable AWS Config and create rules for auditing and compliance purposes. AWS Config can record the configuration changes made to resources like EC2 instances and security groups. The solutions architect can create AWS Config rules to monitor for non-compliant changes, like launching certain instance types or opening security group ports without permission. AWS Config would alert on any violations of these rules.

Requirement Analysis: Need secure, direct connectivity from an on-premises client to an RDS cluster, accessible only when in the office.

VPC with Private Subnets: Ensures the RDS cluster is not publicly accessible, enhancing security.

Site-to-Site VPN: Provides secure, encrypted connection between on-premises office and AWS VPC.

Implementation:

Create a VPC with two private subnets.

Launch the RDS cluster in the private subnets.

Set up a Site-to-Site VPN connection with a customer gateway in the office.

Conclusion: This setup ensures secure and direct connectivity with minimal exposure, meeting the requirement for secure access from the office.

References

AWS Site-to-Site VPN:AWS Site-to-Site VPN Documentation

Amazon RDS:Amazon RDS Documentation

Requirement Analysis: The goal is to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL with minimal application code changes.

Babelfish for Aurora PostgreSQL: Babelfish allows Aurora PostgreSQL to understand SQL Server queries natively, reducing the need for application code changes.

AWS Schema Conversion Tool (SCT): This tool helps in converting the database schema from SQL Server to PostgreSQL.

AWS Database Migration Service (DMS): DMS can be used to migrate data from SQL Server to Aurora PostgreSQL seamlessly.

Combined Approach: Enabling Babelfish addresses the SQL query compatibility, while SCT and DMS handle the schema and data migration.

References

Babelfish for Aurora PostgreSQL:Babelfish Documentation

AWS SCT and DMS:AWS Database Migration Service

Amazon Timestreamis purpose-built for storing and analyzing time-series data like telemetry.

Option Aleverages Timestream, SageMaker for ML, and QuickSight for visualization, meeting all requirements with minimal complexity.

Option Binvolves more complex DynamoDB-EMR integration.

Option Cuses Neptune, which is designed for graph databases, not telemetry data.

Option Dincorrectly uses Athena for visualization instead of QuickSight.

AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security​​.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality​​.

https://aws.amazon.com/premiumsupport/knowledge-center/cloudfront-access-to-amazon-s3/

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-restricting-access-to-s3-overview

Creating a listener rule on theApplication Load Balancer (ALB)to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.

Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.

Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.

AWS References:

ALB Listener Rules

SSE-KMS: Server-side encryption with AWS Key Management Service (SSE-KMS) provides robust encryption of data at rest, integrated with AWS KMS for key management and auditing.

Automatic Key Rotation: By enabling automatic rotation for the KMS keys, the system ensures that keys are rotated annually without manual intervention, meeting compliance requirements.

Logging and Auditing: AWS KMS automatically logs all key usage and management actions in AWS CloudTrail, providing the necessary audit logs.

Implementation:

Create a KMS key with automatic rotation enabled.

Configure the S3 bucket to use SSE-KMS with the created KMS key.

Ensure CloudTrail is enabled for logging KMS key usage.

Operational Efficiency: This solution provides encryption, automatic key management, and auditing in a seamless, fully managed way, reducing operational overhead.

Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless- Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing- Details on the cost-effectiveness of Aurora Serverless.

BothAmazon KinesisandSQS FIFOqueues ensure the sequential processing of messages. By using the payment ID as the partition key in Kinesis or as the message group in the SQS FIFOqueue, messages are processed in order. Both solutions also allow for long-term retention (up to 10 days) of messages, making them suitable for this payment processing use case.

Option A (DynamoDB): DynamoDB does not guarantee message ordering for real-time processing.

Option C (ElastiCache): ElastiCache is for caching, not suitable for sequential message processing.

Option D (Standard SQS queue): A standard SQS queue does not guarantee ordering of messages.

AWS References:

Amazon Kinesis

Amazon SQS FIFO Queues

AWS IAM Identity Center:

IAM Identity Centerprovides centralized access management for multiple AWS accounts within an organization and integrates seamlessly with existing identity providers (IdPs) throughSAML 2.0 federation.

It allows users to authenticate using their existing IdP credentials and gain access to AWS resources without the need to create and manage separate IAM users in each account.

IAM Identity Centeralso simplifies provisioning and de-provisioning users, as it can automatically synchronize users and groups from the external IdP to AWS, ensuring secure and managed access.

Integration with Existing IdP:

The solution involves configuringIAM Identity Centerto connect to the company's IdP using SAML. This setup allows employees to log in with their existing credentials, reducing the complexity of managing separate AWS credentials.

Once connected,IAM Identity Centerhandles authentication and authorization, granting users access to the AWS accounts based on their assigned roles and permissions.

Why the Other Options Are Incorrect:

Option A: Creating separateIAM usersfor each employee is not scalable or efficient. Managing thousands of IAM users across multiple AWS accounts introduces unnecessary complexity and operational overhead.

Option B: Using AWSroot userswith synchronized passwords is a security risk and goes against AWS best practices. Root accounts should never be used for day-to-day operations.

Option D:AWS Resource Access Manager (RAM)is used for sharing AWS resources between accounts, not for federating access for users across accounts. It doesn’t provide a solution for authentication via an external IdP.

AWS References:

AWS IAM Identity Center

SAML 2.0 Integration with AWS IAM Identity Center

By setting upIAM Identity Centerand connecting it to the existing IdP, the company can efficiently manage access for thousands of employees across multiple AWS accounts with a high degree of operational efficiency and security. Therefore,Option Cis the best solution.

Creating aVPC endpointfor S3 and configuring abucket policyto allow access only from the endpoint ensures that only EC2 instances within the VPC can access the S3 bucket. This solutionimproves security by restricting access at the network level without the need for public internet access.

Option A (IAM policies): IAM policies alone cannot restrict access based on the network location.

Option B and D (Encryption): Encryption secures data at rest but does not restrict network access to the bucket.

AWS References:

Amazon S3 VPC Endpoints

This solution allows for secure and least-privilege access with minimal operational overhead.

IAM Identity Center: AWS IAM Identity Center (formerly AWS SSO) enables you to centrally manage access to multiple AWS accounts and applications. By using IAM Identity Center, you can assign permission sets that define what users or groups can access, ensuring that only necessary permissions are granted.

Permission Sets: Permission sets in IAM Identity Center allow you to define granular access controls for specific services, such as Amazon RDS and S3. You can tailor these permissions to meet the needs of different teams, adhering to the principle of least privilege.

Group Management: By assigning users to groups and associating those groups with specific permission sets, you reduce the complexity and overhead of managing individual IAM roles and policies. This method also simplifies compliance and audit processes.

Why Not Other Options?:

Option A (IAM roles): While IAM roles can provide least-privilege access, managing multiple roles and policies across teams increases operational overhead compared to using IAM Identity Center.

Option C (Individual IAM users): Managing individual IAM users and roles can be cumbersome and does not scale well compared to group-based management in IAM Identity Center.

Option D (AWS Organizations with cross-account roles): Creating separate accounts and cross-account roles adds unnecessary complexity and overhead for this use case, where IAM Identity Center provides a more straightforward solution.

AWS References:

AWS IAM Identity Center- Overview and best practices for using IAM Identity Center.

Managing Access Permissions Using IAM Identity Center- Guide on creating and managing permission sets for secure access.

AWS Glue Crawler: AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and load data for analytics. A Glue crawler can automatically discover new data and schema in Amazon S3, making it easy to keep the data catalog up-to-date.

Crawling the Data:

Set up an AWS Glue crawler to scan the S3 bucket containing the clickstream data.

The crawler will automatically detect the schema and create/update the tables in the AWS Glue Data Catalog.

Amazon Athena:

Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

Once the data catalog is updated by the Glue crawler, use Athena to query the clickstream data directly in S3.

Operational Efficiency: This solution leverages fully managed services, reducing operational overhead. Glue crawlers automate data cataloging, and Athena provides a serverless, pay-per-query model for quick data analysis without the need to set up or manage infrastructure.

Amazon FSx for Lustreis a high-performance, fully managed file system that is ideal for applications requiring low-latency access to shared storage, especially in use cases like gaming where high throughput and low latency are essential. It integrates easily with EC2 instances, providing fast and scalable shared storage, and supports custom protocols for specific application needs.

Option A (FSx File Gateway): FSx File Gateway is designed for hybrid cloud storage and is not suited for high-performance gaming workloads.

Option B (EC2 Windows instance): Setting up a file share on a Windows instance would introduce additional administrative overhead and would not provide the necessary performance.

Option C (EFS with Lustre): While Lustre is integrated with FSx, EFS does not natively support Lustre.

AWS References:

Amazon FSx for Lustre

AWS Lambda for Operational Efficiency:

AWS Lambdais a serverless compute service that allows you to run code without provisioning or managing servers. It automatically scales based on the number of invocations and eliminates the need to maintain and monitor EC2 instances, making it far more operationally efficient compared to running a cron job on EC2.

By using Lambda, you pay only for the compute time that your function uses. This is especially beneficial when dealing with lightweight tasks, such as scanning a DynamoDB table and sending email messages once a day.

Amazon DynamoDB:

DynamoDBis a highly scalable, fully managed NoSQL database. The table stores employee start dates, and scanning the table to find the employees who have a work anniversary on the current day is a lightweight operation. Lambda can easily perform this operation using theDynamoDB Scan APIor queries, depending on how the data is structured.

Amazon SNS for Email Notifications:

Amazon Simple Notification Service (SNS)is a fully managed messaging service that supports sending notifications to a variety of endpoints, including email. SNS is well-suited for sendingout email messages to employees, as it can handle the fan-out messaging pattern (sending the same message to multiple recipients).

In this scenario, once Lambda identifies employees who have their work anniversaries, it can use SNS to send the email notifications efficiently. SNS integrates seamlessly with Lambda, and sending emails via SNS is a common pattern for this type of use case.

Event Scheduling:

To automate this daily task, you can schedule the Lambda function usingAmazon EventBridge (formerly CloudWatch Events). EventBridge can trigger the Lambda function on a daily schedule (cron-like scheduling). This avoids the complexity and operational overhead of manually setting up cron jobs on EC2 instances.

Why Not EC2 or SQS?:

Option A & Bsuggest running a cron job on anAmazon EC2instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

Amazon Simple Queue Service (SQS)is ideal for decoupling distributed systems but isn’t necessary in this context because the goal is to send notifications to employees on their work anniversaries. SQS adds unnecessary complexity for this straightforward use case, whereSNSis the simpler and more efficient solution.

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

UsingAWS Lambdacombined withAmazon SNSto send notifications, and scheduling the function withAmazon EventBridgeto run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore,Option Cis the correct and optimal choice.

AWSSecrets Manageris the best solution for securely storing and managing sensitive information, such as usernames and passwords. Secrets Manager provides automatic rotation, fine-grained access control, and encryption of credentials. It is designed to integrate easily with other AWS services, such as CloudFormation, to automate the retrieval of secrets via theBatchGetSecretValue API.

Secrets Manager has a lower operational overhead than manually managing credentials, and it offers features like automatic secret rotation that reduce the need for human intervention.

Option A and C (Parameter Store): While Systems Manager Parameter Store can store secrets, Secrets Manager provides more specialized capabilities for securely managing and rotating credentials with less operational overhead.

Option B and C (AWS Batch): Introducing AWS Batch unnecessarily complicates the solution. Secrets Manager already provides simple API calls for retrieving secrets without needing an additional service.

AWS References:

AWS Secrets Manager

Secrets Manager with CloudFormation

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

AWS Database Migration Service supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora. With AWS Database Migration Service, you can also continuously replicate data with low latency from any supported source to any supported target. For example, you can replicate from multiple sources to Amazon Simple Storage Service (Amazon S3) to build a highly available and scalable data lake solution. You can also consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift. Learn more about the supported source and target databases.https://aws.amazon.com/dms/

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user's geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies- Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing- Information on the different types of load balancers in AWS and when to use them.

To meet the requirement of controlling key rotation with minimal operational overhead, creating acustomer managed key(CMK) in AWS KMS is the optimal solution. With CMKs, you can define custom key rotation policies, ensuring that you retain control over the key lifecycle, including enabling automatic key rotation every year.

Key AWS features:

Custom Key Management: A customer managed key allows you to control the key policies, lifecycle, and enable key rotation for compliance.

Least Operational Overhead: Using a customer managed key simplifies encryption management while offering more flexibility than AWS managed or owned keys.

AWS Documentation: The AWS Well-Architected Framework recommends customer managed keys for environments where key control and flexibility are required​.

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don't provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn't offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

This solution meets the company's requirements with minimal administrative overhead and ensures security and ease of management.

AWS AppConfig: AWS AppConfig is a service designed to manage application configuration in a secure and validated way. It allows you to deploy configurations safely and quickly without affecting the application's performance or availability.

AWS Secrets Manager: AWS Secrets Manager is specifically designed to manage, retrieve, and rotate credentials for databases and other services. It integrates seamlessly with AWS services like Amazon RDS, making it an ideal solution for securely storing and retrieving database credentials. Secrets Manager also provides automatic rotation of credentials, reducing the operational burden.

Why Not Other Options?:

Option B (AWS Lambda + Parameter Store): While AWS Lambda can be used for managing configurations and AWS Systems Manager Parameter Store can store credentials, this approach involves more manual setup and does not offer the same level of integrated management and security as AppConfig and Secrets Manager.

Option C (Encrypted S3 Configuration File): Storing configuration and credentials in S3 files involves more manual management and security considerations, increasing the administrative overhead.

Option D (AppConfig + RDS for credentials): RDS is not designed for storing application credentials; it's better suited for managing database instances and their configurations.

AWS References:

AWS AppConfig- Describes how to use AWS AppConfig for managing application configurations.

AWS Secrets Manager- Provides details on securely storing and retrieving credentials using AWS Secrets Manager.

Option Bensures the use of S3 Object Lock and Versioning to meet compliance for immutability. CloudFront enhances performance while a bucket policy ensures secure access.

Option Alacks immutability safeguards.

Option Cintroduces unnecessary complexity.

Option Dmisses out on additional security benefits offered by CloudFront.

To make the application highly available across regions:

Deploy the application in a different region using a newECS clusterandALBto ensure regional redundancy.

UseRoute 53 failover routingto automatically direct traffic to the healthy region in case of failure.

UseDynamoDB Global Tablesto ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.