SAP-C02 AWS Certified Solutions Architect - Professional Questions and Answers

The best solution is to modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. This will reduce the amount of data scanned by Athena and improve the query performance. Changing the Athena query to view the relevant partitions will also help to filter out unnecessary data. This solution requires minimal operational overhead as it does not involve creating additional resources or changing the log format. References: [AWS WAF Developer Guide], [Amazon Kinesis Data Firehose User Guide] , [Amazon Athena User Guide]

B is correct because the problem is caused by inconsistent query-string casing leading to CloudFront cache misses, and the best place to normalize the request is at the CloudFront edge before cache-key evaluation. CloudFront Functions run at the edge with very low latency and are intended for lightweight request manipulations (for example, normalizing URLs, headers, and query strings) on viewer request events. By sorting query parameters and converting them to lowercase on the viewer request, CloudFront will treat semantically equivalent requests as the same cache key, increasing cache hit ratio and reducing the number of origin calls to API Gateway and downstream Lambda invocations. This directly reduces latency for global users.

Why the other options are less suitable:

A (Lambda@Edge on origin request): Lambda@Edge can also modify requests, but it is heavier-weight than CloudFront Functions for simple normalization logic. Also, doing this on origin request occurs after CloudFront has already decided whether there is a cache hit. If the cache key is still based on the original mixed-case query string at the viewer side, you won’t consistently prevent cache misses. Normalization must happen before cache lookup to maximize cache hits.

C (API Gateway mapping templates): This normalizes the request only after it has already missed in CloudFront and reached the origin. It does not fix CloudFront cache fragmentation, so it does not meet the “minimal latency” goal as effectively.

D (Lambda authorizer): Lambda authorizers are for authorization/authentication decisions. Using an authorizer for query normalization adds unnecessary overhead and still happens at API Gateway (origin), not at CloudFront before cache evaluation.

The company should purchase the same Reserved Instances for an additional 3-year term with All Upfront payment. The company should purchase a 3-year Compute Savings Plan with All Upfront payment in the management account to cover any additional compute costs. This solution will provide the company with the most cost savings because Reserved Instances and Savings Plans are both pricing models that offer significant discounts compared to On-Demand pricing. Reserved Instances are commitments to use a specific instance type and size in a single Region for a one- or three-year term. You can choose between three payment options: No Upfront, Partial Upfront, or All Upfront. The more you pay upfront, the greater the discount1. Savings Plans are flexible pricing models that offer low prices on EC2 instances, Fargate, and Lambda usage, in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a one- or three-year term. You can choose between two types of Savings Plans: Compute Savings Plans and EC2 Instance Savings Plans. Compute Savings Plans apply to any EC2 instance regardless of Region, instance family, operating system, or tenancy, including those that are part of EMR, ECS, or EKS clusters, or launched by Fargate or Lambda. EC2 Instance Savings Plans apply to a specific instance family within a Region and provide the most savings2. By purchasing the same Reserved Instances for an additional 3-year term with All Upfront payment, the company can lock in the lowest possible price for its EC2 instances that run continuously for 3 years. By purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account, the company can benefit from additional discounts on any other compute usage across its member accounts.

The other options are not correct because:

Purchasing a 1-year Compute Savings Plan with No Upfront payment in each member account would not provide as much cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. A 1-year term offers lower discounts than a 3-year term, and a No Upfront payment option offers lower discounts than an All Upfront payment option. Also, purchasing a Savings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

Purchasing a 3-year EC2 Instance Savings Plan with No Upfront payment in the management account to cover EC2 costs in each AWS Region would not provide as much cost savings as purchasing Reserved Instances for an additional 3-year term with All Upfront payment. An EC2 Instance Savings Plan offers lower discounts than Reserved Instances for the same instance family and Region. Also, a No Upfront payment option offers lower discounts than an All Upfront payment option.

Purchasing a 3-year EC2 Instance Savings Plan with All Upfront payment in each member account would not provide as much flexibility or cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. An EC2 Instance Savings Plan applies only to a specific instance family within a Region and does not cover Fargate or Lambda usage. Also, purchasing aSavings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

Evaluate Target Health Setting:

Ensure that the " Evaluate Target Health " setting is enabled for the latency alias resource record sets in Route 53. This setting helps Route 53 determine the health of the resources associated with the alias record and redirect traffic appropriately.

HTTP Health Checks:

Configure HTTP health checks for all weighted resource record sets. Health checks monitor the availability and performance of the web servers, allowing Route 53 to reroute traffic to healthy servers in case of a failure.

Verify that the health checks are correctly set up and associated with the resource record sets. This ensures that Route 53 can detect server failures and redirect traffic to the servers in the other Region.

By enabling the " Evaluate Target Health " setting and configuring HTTP health checks, Route 53 can effectively manage traffic during failover scenarios, ensuring high availability and reliability.

References

AWS Route 53 Documentation on Latency-Based Routing【50】.

AWS Architecture Blog on Cross-Account and Cross-Region Setup【49】.

This solution will meet the requirements with the least operational overhead because it allows the company to modify the existing environment ' s capacity configuration, so it becomes a load-balanced environment type. By selecting all availability zones, the company can ensure that the application is running in multiple availability zones, which can help to improve the availability and scalability of the application. The company can also add a scale-out rule that will run if the average CPU utilization is over 85% for 5 minutes, which can help to mitigate the performance issues. This solution does not require creating new Elastic Beanstalk environments or rebuilding the existing one, which reduces the operational overhead.

You can refer to the AWS Elastic Beanstalk documentation for more information on how to use this service:https://aws.amazon.com/elasticbeanstalk/You can refer to the AWS documentation for more information on how to use autoscaling:https://aws.amazon.com/autoscaling/

The company is building an active-active, multi-Region architecture for an ECS-based web application behind ALBs, with shared container images in ECR and a relational database in Aurora MySQL. The goal is to duplicate the setup in a second Region with minimal operational overhead while allowing minor replication latency.

A is required because ACM certificates are Regional for use with Regional resources like an Application Load Balancer. An ALB in the secondary Region needs an ACM certificate that exists in that same Region to terminate HTTPS. Requesting (or importing) a certificate in the secondary Region and attaching it to the ALB HTTPS listener is the standard managed approach with low operational overhead.

B is not correct because ACM certificates are not shared “across Regions” for direct use by a Regional ALB in another Region. Sharing via AWS RAM is used for sharing resources across accounts within a Region (and for supported resource types), but it does not provide a mechanism to use an ACM certificate issued in one Region directly on an ALB in a different Region.

D is required because the application runs in both Regions and must be able to pull container images locally with minimal operational work. Amazon ECR supports cross-Region replication so images pushed in the primary Region can be replicated automatically to the secondary Region’s ECR repositories. This avoids building custom copy pipelines and reduces the chance of drift. Once replication is enabled, updating images in the primary Region can automatically populate the secondary Region repository.

C is not the best fit. A VPC endpoint for ECR is about private connectivity to ECR within a Region. It does not solve the multi-Region image availability problem by itself, and it introduces the idea of pulling images from the primary Region, which is unnecessary and less optimal when cross-Region replication can keep images local to each Region.

E is required because the database must support multi-Region application operation with tolerable replication latency. Aurora Global Database is the managed feature designed to replicate an Aurora cluster to a secondary Region with low-latency replication and minimal operational overhead compared to custom replication. Enabling write forwarding allows applications in the secondary Region to send write requests to the secondary cluster endpoint, with Aurora forwarding those writes to the primary writer. This reduces application changes compared to forcing all writes to go directly to the primary Region endpoints, and it supports an active-active application deployment pattern where both Regions can accept traffic.

F is not correct because “cross-Region writes between DB clusters” and “multiple writer instances in the primary Region” do not provide a managed, multi-Region, multi-writer Aurora design as described. Aurora’s managed multi-Region pattern is Aurora Global Database with a single primary writer in one Region (with the option to promote a secondary Region during failover) and optional write forwarding from a secondary Region to the primary. The option’s described cross-Region multi-writer behavior is not a standard supported Aurora configuration and would not be the least operational overhead approach.

Therefore, the least operational overhead combination that correctly addresses HTTPS termination in the secondary Region, image availability in both Regions, and managed multi-Region database replication is A, D, and E.

(https://stackoverflow.com/questions/50250114/athena-vs-redshift-spectrum)

The company wants backups to be stored in an isolated central account designed for WORM storage. With AWS Organizations, AWS Backup supports centralized, cross-account backup management through delegated administration, backup policies, and cross-account backup vault usage. This provides the least operational overhead and scales to dozens of databases.

A key constraint is encryption. The databases are encrypted with the default RDS AWS KMS key. AWS-managed KMS keys (including the default RDS KMS key) are not customer-managed and generally cannot be shared across accounts for cross-account snapshot copy and cross-account backup workflows that require key sharing and explicit grants. For cross-account backup copies, the source backups must be encrypted with a customer managed KMS key that can be shared (via key policy and grants) with the destination account. Therefore, the company must move away from using the default RDS AWS-managed key for encryption of snapshots that will be copied across accounts.

Option A uses the correct managed approach: enable trusted access and backup policies in Organizations, make the central account the delegated administrator, and use AWS Backup to manage cross-account backups into a central backup vault. It also correctly introduces a customer managed KMS key in the central account for the backup vault and shares that key with the production account. Because the existing databases are encrypted with the default RDS AWS-managed key, option A includes the necessary remediation step: restore (or otherwise recreate) the databases so they are encrypted with the shared customer managed KMS key, enabling future backups and copies to be encrypted with a shareable key and stored in the central vault.

Option B is not workable because it requires “sharing the default RDS KMS key” with another account and using it for the central vault. AWS-managed keys are not shared and are not controlled by customers in a way that supports this cross-account backup model. Therefore, this option fails the encryption requirement.

Option C and option D rely on decrypting snapshots, copying them as unencrypted, and then re-encrypting. This is not a valid or safe operational pattern for RDS automated backups at scale, and it conflicts with typical constraints around snapshot encryption transitions. It also introduces substantial custom automation and operational overhead, and it undermines the objective of a controlled WORM backup account by manipulating encryption state through custom code. Additionally, “decrypting snapshots” is not how encrypted RDS snapshots are typically handled; encrypted snapshots remain encrypted and are copied re-encrypted with a different KMS key that the destination can use, which requires a customer managed key and proper permissions.

Therefore, the successful and scalable solution is to centralize backups with AWS Backup and ensure the databases are encrypted with a customer managed KMS key that can be shared cross-account, as described in option A.

Creating a single transit gateway in eu-west-1 allows for centralized and cost-effective connectivity between the VPCs involved, without the complexity of creating multiple peering connections or transit gateways.

This approach also allows for selective attachment of only the required VPCs, meeting the strict security and access requirements.

Route table configuration ensures traffic flows properly through the transit gateway, providing connectivity for the application without exposing other VPC resources.

This solution is the most cost-effective and operationally efficient choice for secure inter-Region communication.

https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/send-a-notification-when-an-iam-user-is-created.html

C: Multipart uploads can leave incomplete parts behind, which incur storage costs. Expiring them after 7 days minimizes waste and saves cost.

E: Since objects are infrequently accessed after 180 days, transitioning toS3 Standard-IAis cost-effective, especially for large files > 128 KB (your 100 MB+ files qualify).

Ais for shifting download cost, not reducing your S3 storage expenses.

Bhelps with upload speed butincreases cost.

Dis too aggressive; Glacier is not suited for access patterns within the first few days.

The analytics team wants to copy only a subset of the data, run the copy as soon as data is ready, and receive notifications when the process completes. AWS DataSync supports the use of manifest files to control exactly which files or objects are transferred. By generating a manifest file on premises that lists only the relevant data and uploading that manifest to Amazon S3, the company can precisely limit what DataSync copies, which reduces data transfer costs and processing overhead.

Option A satisfies the requirement to identify only analytics-relevant data by creating and uploading a manifest file that lists the objects to be transferred. This avoids copying unnecessary data and is cost-effective.

Option D uses Amazon S3 Event Notifications to trigger an AWS Lambda function as soon as the manifest file is uploaded. The Lambda function starts the DataSync task and references the manifest file. This ensures that the copy process begins immediately after data generation, rather than waiting for a fixed schedule, which meets the “as soon as possible” requirement.

Option E provides a fully managed and scalable notification mechanism. AWS DataSync publishes task execution state changes as events. Amazon EventBridge can capture SUCCESS or ERROR states and route those events to Amazon SNS, which can then send email notifications. This approach avoids custom polling logic and minimizes operational overhead.

Option B and C introduce Amazon DynamoDB unnecessarily. DataSync can directly consume a manifest file from Amazon S3, so loading manifest data into DynamoDB and orchestrating copy logic manually adds cost and complexity without benefit.

Option F relies on a custom Lambda function to monitor task state changes, which increases operational overhead compared to the native EventBridge integration with DataSync.

Therefore, using S3-based manifests, event-driven invocation of DataSync, and EventBridge-based notifications is the most cost-effective and operationally efficient solution.

https://docs.aws.amazon.com/codedeploy/latest/userguide/integrations-aws-auto-scaling.html

Set Up AWS Elastic Disaster Recovery:

Navigate to the AWS Elastic Disaster Recovery (DRS) console.

Configure the Elastic Disaster Recovery service to replicate your on-premises VMware vSphere VMs to Amazon EC2 instances. This involves installing the AWS Replication Agent on your VMs.

Configure Replication Settings:

Define the replication settings, including the Amazon EC2 instance type and the Amazon EBS volume configuration. Ensure that the replication frequency meets your Recovery Point Objective (RPO) of 5 minutes.

Monitor Data Replication:

Monitor the initial data replication process in the Elastic Disaster Recovery console. Once the initial sync is complete, the status should show as " Healthy " indicating that the data replication is up-to-date and within the RPO requirements.

Disaster Recovery (Failover):

In the event of a disaster, initiate a failover from the Elastic Disaster Recovery console. This will launch the replicated Amazon EC2 instances using the Amazon EBS volumes with the latest data.

Failback Process:

Once the on-premises environment is restored, perform a failback operation to synchronize the data from AWS back to your on-premises VMware environment. Use the failback client provided by AWS Elastic Disaster Recovery to ensure data consistency and minimal downtime during the failback process.

Using AWS Elastic Disaster Recovery provides a low-overhead, automated solution for disaster recovery that ensures minimal data loss and meets the RPO requirement of 5 minutes​(Amazon Web Services, Inc.)​​(AWS Documentation)​.

The company should deploy a new Amazon Elastic File System (Amazon EFS) Multi-AZ file system. The company should configure the file system for 75 MiBps of provisioned throughput. The company should implement replication to a file system in the DR Region. This solution will meet the requirements because Amazon EFS is a serverless, fully elastic file storage service that lets you share file data without provisioning or managing storage capacity and performance. Amazon EFS is built to scale on demand to petabytes without disrupting applications, growing and shrinking automatically as you add and remove files1. By deploying a new Amazon EFS Multi-AZ file system, the company can create a single location for updates to application data for all instances. A Multi-AZ file system replicates data across multiple Availability Zones (AZs) within a Region, providing high availability and durability2. By configuring the file system for 75 MiBps of provisioned throughput, the company can ensure that it meets the peak operations requirement of 225 MiBps of read throughput. Provisioned throughput is a feature that enables you to specify a level of throughput that the file system can drive independent of the file system’s size or burst credit balance3. By implementing replication to a file system in the DR Region, the company can make a copy of the data available in another AWS Region for disaster recovery. Replication is a feature that enables you to replicate data from one EFS file system to another EFS file system across AWS Regions. The replication process has an RPO of less than 1 hour.

The other options are not correct because:

Deploying a new Amazon FSx for Lustre file system would not provide a single location for updates to application data for all instances. Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance storage for compute workloads. However, it does not support concurrent write access from multiple instances. Using AWS Backup to back up the file system to the DR Region would not provide real-time replication of data. AWS Backup is a service that enables you to centralize and automate data protection across AWS services. However, it does not support continuous data replication or cross-Region disaster recovery.

Deploying a General Purpose SSD (gp3) Amazon Elastic Block Store (Amazon EBS) volume with 225 MiBps of throughput would not provide a single location for updates to application data for all instances. Amazon EBS is a service that provides persistent block storage volumes for use with Amazon EC2 instances. However, it does not support concurrent access from multiple instances, unless Multi-Attach is enabled. Enabling Multi-Attach for the EBS volume would not provide Multi-AZ resilience or cross-Region replication. Multi-Attach is a feature that enables you to attach an EBS volume to multiple EC2 instances within the same Availability Zone. Using AWS Elastic Disaster Recovery to replicate the EBS volume to the DR Region would not provide real-time replication of data. AWS Elastic Disaster Recovery (AWS DRS) is a service that enables you to orchestrate and automate disaster recovery workflows across AWS Regions. However, it does not support continuous data replication or sub-hour RPOs.

Deploying an Amazon FSx for OpenZFS file system in both the production Region and the DR Region would not be as simple or cost-effective as using Amazon EFS. Amazon FSx for OpenZFS is a fully managed service that provides high-performance storage with strong data consistency and advanced data management features for Linux workloads. However, it requires more configuration and management than Amazon EFS, which is serverless and fully elastic. Creating an AWS DataSync scheduled task to replicate the data from the production file system to the DR file system every 10 minutes would not provide real-time replication of data. AWS DataSync is a service that enables you to transfer data between on-premises storage and AWS services, or between AWS services. However, it does not support continuous data replication or sub-minute RPOs.

Resource-based policies are policies that you attach to a resource, such as an IAM role, to specify who can access the resource and what actions they can perform on it1. Identity-based policies are policies that you attach to an IAM user, group, or role to specify what actions they can perform on which resources2. Trust policies are special types of resource-based policies that define which principals (such as IAM users or roles) can assume a role3.

To allow an IAM user in Account A to assume a role in Account B, the solutions architect needs to do the following:

Configure the resource-based policy on the target role in Account B to allow the action sts:AssumeRole for the IAM user in Account A. This policy grants permission to the IAM user to assume the role4.

Configure the identity-based policy on the user in Account A to allow the action sts:AssumeRole for the target role in Account B. This policy grants permission to the user to perform the action of assuming the role5.

Configure the trust policy on the target role in Account B to allow the principal of the IAM user in Account A. This policy defines who can assume the role.

Resource-based policies

Identity-based policies

Trust policies

Granting a user permissions to switch roles

Switching roles

[Modifying a role trust policy]

Gateway VPC Endpoint for S3:

Create a gateway VPC endpoint for Amazon S3 in your VPC. This allows instances in your VPC to communicate with Amazon S3 without going through the internet, reducing data transfer costs and improving security.

Add Spot Instances to ECS Cluster:

Add another ECS capacity provider that uses an Auto Scaling group of Spot Instances. Configure this new capacity provider to share the load with the existing On-Demand Instances by setting an appropriate weight in the capacity provider strategy. Spot Instances offer significant cost savings compared to On-Demand Instances.

Configure Capacity Provider Strategy:

Adjust the ECS service ' s capacity provider strategy to utilize both On-Demand and Spot Instances effectively. This ensures a balanced distribution of tasks across both instance types, optimizing cost while maintaining availability.

By implementing a gateway VPC endpoint for S3 and incorporating Spot Instances into the ECS cluster, the company can significantly reduce operational costs without compromising on the availability or performance of the platform.

References

AWS Cost Optimization Blog on VPC Endpoints

AWS ECS Documentation on Capacity Providers

https://docs.aws.amazon.com/firehose/latest/dev/creating-the-stream-to-splunk.html

The workload consists of large batch-processing simulation jobs that read 15–20 GB per job from Amazon S3, write results back to S3, are not time sensitive, and can tolerate interruptions. These characteristics are ideal for using EC2 Spot Instances, which provide steep discounts compared to On-Demand pricing in exchange for potential interruptions.

AWS Batch is designed specifically for batch and simulation workloads. It can dynamically provision compute resources, manage job queues, retry failed jobs, and integrate tightly with EC2 Spot Instances. Using an AWS Batch compute environment that is configured to use EC2 Spot Instances with the SPOT_CAPACITY_OPTIMIZED allocation strategy allows AWS Batch to choose the lowest interruption-risk Spot capacity pools automatically while still giving the largest cost savings.

Option B leverages these capabilities. It creates an AWS Batch compute environment using only Spot Instances and uses the SPOT_CAPACITY_OPTIMIZED allocation strategy to select capacity across instance types with lower interruption rates. Since the simulations are not time sensitive and can withstand interruptions, the risk of Spot interruptions is acceptable, and AWS Batch will re-run interrupted jobs as needed. This combination provides the most cost-effective solution while minimizing operational overhead for job scheduling and capacity management.

Option A uses Lambda with Step Functions. Lambda has limits on memory, runtime, and payload size, making it a poor fit for processing 15–20 GB of data per job. In addition, the concurrency and invocation cost for large-scale, long-running simulation workloads would be higher and less cost-effective than EC2 Spot Instances.

Option C mixes On-Demand and Spot Instances. While this can provide reliability for more time-critical workloads, it is less cost-effective than a pure Spot configuration when the workload explicitly can tolerate interruptions and is not time sensitive.

Option D uses Amazon EKS with a mix of On-Demand and Spot Instances. While this can be made to work, it requires managing Kubernetes clusters, node groups, and job scheduling, which introduces more operational complexity than using AWS Batch, and the inclusion of On-Demand capacity reduces cost-effectiveness compared to pure Spot-based batch processing.

Therefore, using AWS Batch with EC2 Spot Instances and the SPOT_CAPACITY_OPTIMIZED allocation strategy (option B) is the most cost-effective solution for this tolerant, batch-processing workload.

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

Deploy AWS DataSync Agent:

Install the DataSync agent on your on-premises environment. This can be done by downloading the agent as a virtual appliance and deploying it on VMware ESXi, Hyper-V, or KVM hypervisors.

Configure Source and Destination Locations:

Set up the source location pointing to your on-premises storage where the software images are currently stored.

Configure the destination location to point to your Amazon S3 bucket in the ap-northeast-1 Region.

Create and Schedule DataSync Tasks:

Create a DataSync task to automate the transfer process. This task will specify the source and destination locations and set options for how the data should be transferred.

Schedule the task to run at intervals that suit your data transfer requirements, ensuring new images are transferred as they are created.

Encryption in Transit:

AWS DataSync automatically encrypts data in transit using TLS, ensuring that your data is secure during the transfer process.

Monitoring and Management:

Use the DataSync console or the AWS CLI to monitor the progress of your data transfers and manage the tasks.

AWS DataSync is an efficient solution that automates and accelerates the process of transferring large amounts of data to AWS, handling encryption, data integrity checks, and optimizing network usage without requiring custom development.

References

AWS Storage Blog on DataSync【40】.

AWS DataSync Documentation【41】.

Option A, Deploy the security tool on EC2 instances in a new Auto Scaling group in the existing VPC, allows the company to use its existing security tool while still running it within the AWS environment. This ensures that all packets coming in and out of the VPC are inspected by the security tool in real time. Option D, Provision a Gateway Load Balancer for each Availability Zone to redirect the traffic to the security tool, allows for high availability within an AWS Region. By provisioning a Gateway Load Balancer for each Availability Zone, the traffic is redirected to the security tool in the event of any failures or outages. This ensures that the security tool is always available to inspect the traffic, even in the event of a failure.

The correct answer is A because the control must prevent creation of new EC2 instances and EBS volumes unless the required Project tag is supplied in the launch request. The relevant API action is ec2:RunInstances, not ec2:StartInstances. AWS supports enforcing tags at resource creation by using request tag condition keys such as aws:RequestTag/Project. A deny statement with the Null condition set to true blocks the request when the tag is missing. aws:CostAllocationTag is not the correct condition key for authorization at launch time; cost allocation tags are for billing categorization after tags exist. AWS tagging guidance specifically shows deny policies for ec2:RunInstances when a required cost allocation-style tag is not present in the request. (AWS Documentation)

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The best solution is to deploy an Amazon RDS instance with a cross-Region read replica in a secondary Region. This will provide the company with a database solution that can fail over to the secondary Region in case of a disaster. The read replica will have minimal replication lag and can be promoted to become the primary in less than 10 minutes, meeting the RTO requirement. The RPO requirement of less than 5 minutes can also be met by using synchronous replication within the primary Region and asynchronous replication across Regions. This solution will also have the lowest cost compared to the other options, as it does not involve additional services or resources. References: [Amazon RDS User Guide], [Amazon Aurora User Guide]

B is correct because Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked with Amazon S3 using data repository integration. This allows the EC2-based processing engine (which requires POSIX file operations) to read/write files through a mounted Lustre file system while automatically importing objects from S3 and exporting results back to S3 using Lustre’s S3 integration policies. This satisfies all requirements: POSIX file access, no need to change the application to use the S3 API, and automated synchronization for both inputs and outputs.

Why the other options are incorrect:

A: DataSync is designed for scheduled or task-based data transfers. It does not provide a mounted POSIX file system interface for the application to read/write “in place,” and the “without an agent” requirement is not a fit for an EC2-hosted POSIX workflow that needs continuous file semantics.

C: Amazon EFS is POSIX and mountable, but “data repository associations to S3” is not an EFS capability in the way described. EFS does not provide native bidirectional automatic sync with S3 using import/export policies like FSx for Lustre’s S3 integration.

D: S3 File Gateway provides a file interface backed by S3, but keeping cache coherent with explicit RefreshCache calls is operationally complex and not truly “automatic synchronization” in the sense required. It also introduces caching behaviors that can complicate immediate availability expectations.

Integrate ALB with OIDC IdP:

In the AWS Management Console, navigate to the Application Load Balancer (ALB) settings.

Configure the ALB to use the OpenID Connect (OIDC) IdP for authentication. This ensures that all requests routed through the ALB are authenticated using the IdP.

Set Up Authentication Rules:

Create a listener rule on the ALB that requires authentication. This rule will forward requests to the IdP for user authentication before allowing access to the backend services.

Restrict Unauthenticated Access:

Ensure the ALB only forwards requests to backend services if the user is authenticated. Unauthenticated requests should be blocked or redirected to the IdP for authentication.

Update CloudFront Configuration:

Modify the CloudFront distribution to forward authenticated requests to the ALB. Ensure that the ALB and API Gateway accept only requests coming through the CloudFront distribution to enforce consistent authentication and security.

By enforcing authentication at the ALB level, you ensure that all backend services are accessed only by authenticated users, enhancing the overall security of the web application

Log Tenant ID and RCUs/WCUs:

Update the AWS Lambda functions to log the tenant ID and the number of Read Capacity Units (RCUs) and Write Capacity Units (WCUs) consumed from DynamoDB for each transaction.This data will be logged to Amazon CloudWatch Logs.

Calculate Tenant Costs:

Deploy an additional Lambda function that reads the logs from CloudWatch Logs, calculates the RCUs and WCUs used by each tenant, and then uses the AWS Cost Explorer API to retrieve the overall cost of DynamoDB usage. This function will then allocate the costs to each tenant based on their usage.

Scheduled Cost Calculation:

Create an Amazon EventBridge rule to trigger the cost calculation Lambda function at regular intervals (e.g., daily or hourly). This ensures that cost allocation is continuously updated and tenants are billed accurately based on their consumption.

This solution minimizes operational effort by automating the cost allocation process and ensuring that the company can accurately bill tenants based on their resource consumption.

References

AWS Cost Explorer Documentation

Amazon CloudWatch Logs Documentation

AWS Lambda Documentation

Comprehensive and Detailed in Depth Explanation:

C is correct because Amazon EFS offers a scalable, elastic file system that can be mounted on both on-premises servers (via AWS Direct Connect or VPN) and EC2 instances. This allows real-time access to shared content with no migration downtime.

D is correct because AWS Snowball Edge Storage Optimized devices are designed for transferring petabyte-scale data (up to 80 TB per device). It’s a best practice for moving 200 TB of archival data when speed and bandwidth are constraints.

Identify Proxy EC2 Instances:

Determine which EC2 instances in the private subnets are running the proxy server software.

Disable Source/Destination Checks:

For each of these EC2 instances, go to the AWS Management Console.

Navigate to the EC2 dashboard, select the instance, and choose " Actions " > " Networking " > " Change Source/Dest.Check " .

Disable the source/destination check for these instances.

Disabling source/destination checks allows the EC2 instances to route traffic appropriately, enabling them to function as network appliances or proxies. This ensures that traffic from other instances in the private subnets can be routed through the proxy instances to the internet, meeting the company ' s security requirements.

References

Amazon EC2 User Guide on Source/Destination Checks

It is the fastest when it comes to rollback and deploying changes every hour

https://aws.amazon.com/s3/features/access-points/

https://aws.amazon.com/blogs/storage/managing-amazon-s3-access-with-vpc-endpoints-and-s3-access-points/

Amazon Connect is a cloud-based contact center service that allows you to set up a virtual call center for your business. It provides an easy-to-use interface for managing customer interactions through voice and chat. Amazon Connect integrates with other AWS services, such as Amazon S3 and Amazon Kinesis, to help you collect, store, and analyze customer data for insights into customer behavior and trends. On the other hand, Amazon Pinpoint is a marketing automation and analytics service that allows you to engage with your customers across different channels, such as email, SMS, push notifications, and voice. It helps you create personalized campaigns based on userbehavior and enables you to track user engagement and retention. While both services allow you to communicate with your customers, they serve different purposes. Amazon Connect is focused on customer support and service, while Amazon Pinpoint is focused on marketing and engagement.

Using AWS Backup to create a scheduled daily backup plan for the EC2 instances will enable taking snapshots of the EC2 instances and their attached EBS volumes1. Configuring the backup task to copy the backups to a vault in the secondary Region will enable maintaining backups in a separate Region1. In the event of a failure, launching the CloudFormation template will enable deploying the network configuration in the secondary Region2. Restoring the instance volumes and configurations from the backup vault will enable recovering the EC2 instances and their data1. Transferring usage to the secondary Region will enable resuming operations2.

When an IAM user appears to have the correct permissions but still receives Access Denied errors, especially in an AWS Organizations environment, the effective permissions must be evaluated across all permission layers. In AWS, permissions are the intersection of all applicable controls, and an explicit deny at any layer overrides any allow.

First, because the company uses AWS Organizations, service control policies (SCPs) must be evaluated. SCPs define the maximum permissions that accounts or organizational units can have. Even if an IAM user or IAM policy allows an action, an SCP attached to the account or OU can explicitly or implicitly deny that action. If an SCP does not allow s3:ListAllMyBuckets or related S3 read actions, the IAM user will not be able to list buckets in the S3 console and will see no buckets at all. Therefore, checking the SCPs applied to the OU or account is a required troubleshooting step.

Second, IAM permissions boundaries can further restrict the effective permissions of an IAM user. A permissions boundary defines the maximum permissions that an IAM user can exercise, regardless of what their attached policies allow. If the permissions boundary does not include the required Amazon S3 read-only permissions (such as s3:ListAllMyBuckets or s3:GetBucketLocation), the user will receive access denied errors even though the user’s IAM policy appears to allow read-only access. Reviewing whether a permissions boundary is attached, and whether it allows the necessary S3 actions, is essential.

Option A (checking bucket policies) can be relevant for access to specific buckets or objects, but a user seeing no buckets at all in the S3 console is more commonly caused by missing or denied account-level list permissions rather than individual bucket policies. Bucket policies generally do not control the ability to list all buckets in an account unless they contain explicit denies, which is less common as a first troubleshooting step in an Organizations setup.

Option B (checking ACLs) is less relevant because ACLs primarily control object-level and bucket-level access and are not typically used to manage console-level visibility of all buckets in an account. ACLs also do not commonly block the ListAllMyBuckets action.

Option E is incorrect because IAM users do not require IAM roles to access AWS services. Roles are assumed by users or services, but the presence or absence of a role attached to an IAM user does not affect the user’s direct permissions.

Therefore, the most appropriate troubleshooting steps are to check the SCPs applied through AWS Organizations and to verify whether an IAM permissions boundary is restricting the user’s effective permissions.

To improveresiliency and fault tolerance, place aNAT Gateway in each AZ, and route each private subnet to its local NAT gateway. This ensures local AZ failure won ' t break internet access.

VPC NAT Gateway High Availability

https://docs.aws.amazon.com/waf/latest/developerguide/marketplace-managed-rule-groups.html

Amazon Kinesis Data Streams is a service that enables real-time data ingestion and processing. Amazon DynamoDB is a NoSQL database that does not require fixed schemas for storage. By using Kinesis Data Streams and DynamoDB, the company can send the JSON data to a database that can handle schemaless data in real time. References:

https://docs.aws.amazon.com/streams/latest/dev/introduction.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html

RDS Proxyoptimizes and pools DB connections. By combining this withPrivateLink + NLB, other accounts can securely access the proxy endpoint without going over the internet or setting up peering for every new account.

RDS Proxy Docs

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html#placement- groups-cluster

" This operation can be called only from the organization ' s management account. Member accounts can remove themselves with LeaveOrganization instead. " https://docs.aws.amazon.com/organizations/latest/APIReference/API_RemoveAccountFromOrganization.html

This solution will allow the external auditors to have read-only access to the company ' s AWS account while being compliant with AWS security best practices. By creating an IAM role, which is a secure and flexible way of granting access to AWS resources, and trusting the auditors ' AWS account, the company can ensure that the auditors only have the permissions that are required for their role and nothing more. Assigning a unique external ID to the role ' s trust policy, it will ensure that only the auditors ' AWS account can assume the role.

https://docs.aws.amazon.com/global-accelerator/latest/dg/about-accelerators.alb-accelerator.html Option A is wrong. AWS WAF does not support associating with NLB. https://docs.aws.amazon.com/waf/latest/developerguide/waf-chapter.html Option B is wrong. An ALB does not support an Elastic IP address.https://aws.amazon.com/elasticloadbalancing/features/

By adding two more NAT gateways (one per Availability Zone), each private subnet has direct access to a NAT gateway in its own AZ.

This increases network resiliency because even if a single NAT gateway becomes unavailable, the others remain operational, ensuring consistent internet connectivity for the private EC2 instances.

This follows AWS best practices for highly available network architecture in multi-AZ deployments.

https://aws.amazon.com/premiumsupport/knowledge-center/cross-account-access-s3/

You can use a Glue crawler to populate the AWS Glue Data Catalog with tables. The Lambda function can be triggered using S3 event notifications when object create events occur. The Lambda function will then trigger the Glue ETL job to transform the records masking the sensitive data and modifying the output format to JSON. This solution meets all requirements.

To enhance the network connectivity solution ' s availability, fault tolerance, and security in a cost-effective manner, adding a dynamic private IP AWS Site-to-Site VPN as a secondary path is a viable option. This VPN serves as a resilient backup for the Direct Connect connection, ensuring continuous data flow even if the primary path fails. Implementing MACsec (Media Access Control Security) on the Direct Connect connection further secures the data in transit by providing encryption, thus addressing the security requirement. This solution strikes a balance between cost and operational efficiency, avoiding the higher expenses associated with provisioning an additional Direct Connect connection.

AWS Documentation on AWS Direct Connect and AWS Site-to-Site VPN provides insights into setting up resilient and secure network connections. Additionally, information on MACsec offers guidance on how to implement encryption for Direct Connect connections, aligning with best practices for secure and highly available network architectures.

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/stacksets-orgs-manage-auto-deployment.html

Configuring an S3 Lifecycle policy to expire incomplete multipart uploads after 7 days prevents storage of partially uploaded objects, avoiding unnecessary costs from failed uploads.

Additionally, implementing a lifecycle policy to transition objects to S3 Standard-IA after 180 days ensures older videos that are rarely accessed move to a lower-cost storage class, significantly reducing storage costs.

These measures are aligned with AWS cost optimization best practices for S3 data lifecycle management.

Answer B (AWS Global Accelerator) best meets both requirements: fastest failover from application failures and the lowest response time for global users. Global Accelerator is designed for global, latency-sensitive, highly available applications. It uses static Anycast IP addresses so users enter the AWS network at the nearest edge location, then traffic is carried over the AWS global backbone to the closest healthy regional endpoint (in this case, each Region’s ALB). It continuously performs health checks and can rapidly shift traffic away from unhealthy endpoints, which helps “handle application failures quickly” without being constrained by DNS caching behavior.

Why the other options are less suitable:

C (Route 53 latency-based routing + health checks) can improve routing and failover, but it is still DNS-based. DNS-based failover can be delayed by DNS resolver caching and TTL propagation, meaning failover may not be as fast or as consistent as Global Accelerator for rapid recovery.

A (CloudFront with origin group) can provide origin failover and performance benefits mainly for cacheable or edge-optimizable content. For a payment processing API (typically dynamic and not cache-friendly), CloudFront is not the primary service for achieving the least response time globally with fast multi-Region failover compared with Global Accelerator’s Anycast + edge routing model.

D (AWS Cloud WAN) addresses private network connectivity and centralized network policy across Regions/VPCs. It does not directly optimize public user-to-application latency or provide the same kind of edge-based, Anycast-driven rapid failover for internet-facing API endpoints.

You can use the management account of the organization in AWS Billing and Cost Management console to turn off RI sharing for the HR department ' s production AWS account. This will prevent other departments from sharing the RI discounts and ensure that only the HR department can use the RIs purchased in their production account.

https://aws.amazon.com/blogs/aws/new-attributes-based-access-control-with-aws-single-sign-on/

The requirement is to continuously scan all AWS resources in this solution for security vulnerabilities with the least operational overhead. The environment includes Amazon EKS worker nodes in a managed node group and an Amazon ECR repository that stores container images used by the EKS cluster.

Amazon Inspector is a managed vulnerability scanning service that integrates directly with multiple AWS resource types. It can automatically discover Amazon EC2 instances (including EKS managed node group instances) and perform continuous vulnerability assessments against them, using software inventory and known CVE databases. Inspector also integrates with Amazon ECR to scan container images for vulnerabilities when images are pushed to the repository and periodically thereafter, providing continuous assessment of image security posture without requiring separate scanners or infrastructure.

By activating Amazon Inspector for the account and region, the service will automatically start tracking supported resources, including EKS-managed EC2 instances and ECR repositories, and will begin scanning them for known vulnerabilities. Findings are provided through the Inspector console and can be integrated with other services such as AWS Security Hub or Amazon EventBridge for further aggregation and automation, but no separate infrastructure deployment or maintenance is required.

Option B, therefore, provides end-to-end vulnerability scanning for both the EKS nodes and the ECR images with minimal operational overhead, because it leverages a fully managed AWS service that automatically discovers and scans supported resources.

Option A is not correct because AWS Security Hub does not itself perform vulnerability scanning. Security Hub aggregates and normalizes security findings from various AWS services, including Amazon Inspector, GuardDuty, and others. It is a security posture management and aggregation service, not a vulnerability scanner.

Option C introduces additional operational overhead by requiring the company to provision, configure, secure, and maintain a separate EC2 instance running a third-party vulnerability scanning tool. While ECR basic scan-on-push can detect some image vulnerabilities, the EC2-based scanner must be managed, updated, and scaled by the customer, which violates the requirement for the least operational overhead when a fully managed alternative exists.

Option D is incorrect because the Amazon CloudWatch agent is used for metrics and log collection from EC2 instances and other environments; it does not perform vulnerability scanning or CVE analysis. Configuring CloudWatch for monitoring and logging does not meet the requirement to continuously scan for security vulnerabilities. ECR basic scans also provide only image-level scanning and do not cover the EKS nodes themselves.

Therefore, enabling Amazon Inspector to scan both the EKS nodes and the ECR repository, as described in option B, is the solution that meets the continuous vulnerability scanning requirement with the least operational overhead.

The performance symptom is classic Lambda cold-start variability. The API is used sporadically, so Lambda execution environments may be recycled between requests. The first request takes longer because Lambda must initialize an execution environment; subsequent identical requests are faster because the environment is already warm. Provisioned concurrency keeps execution environments initialized and ready to respond, producing more consistent latency. AWS Application Auto Scaling can manage provisioned concurrency with target tracking, which helps align provisioned capacity with usage patterns. DynamoDB on-demand mode already scales automatically for table throughput, and DAX would mainly help read-heavy caching scenarios, not Lambda initialization delay. CloudFront may reduce network latency for cacheable content, but it does not remove Lambda cold starts for dynamic API calls. (AWS Documentation)

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In this solution, the company can use Amazon SES to send email messages, which will minimize operational overhead as SES is a fully managed service that handles sending and receiving email messages. The company can store the email template on Amazon SES with parameters for the customer data and use an AWS Lambda function to call the SendTemplatedEmail API operation, passing in the customer data to replace the parameters and the email destination. This solution eliminates the need to set up and manage an SMTP server on EC2 instances, which can be costly and time-consuming.

A. High performance computing (HPC) workload cluster should be in a single AZ.

C. Elastic Fabric Adapter (EFA) is a network device that you can attach to your Amazon EC2 instances to accelerate High Performance Computing (HPC)

F. Amazon FSx for Lustre - Use it for workloads where speed matters, such as machine learning, high performance computing (HPC), video processing, and financial modeling.

Cluster – packs instances close together inside an Availability Zone. This strategy enables workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication that is typical of HPC applications.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html

The company should use Migration Evaluator to generate a list of servers and build a report for a business case. The company should use AWS Migration Hub to view the portfolio and use AWS Application Discovery Service to gain an understanding of application dependencies. This solution will meet the requirements because Migration Evaluator is a migration assessment service that helps create a data-driven business case for AWS cloud planning and migration. Migration Evaluator provides a clear baseline of what the company is running today and projects AWS costs based on measured on-premises provisioning and utilization1. Migration Evaluator can use an agentless collector to conduct broad-based discovery or securely upload exports from existinginventory tools2. Migration Evaluator integrates with AWS Migration Hub, which is a service that provides a single location to track the progress of applicationmigrations across multiple AWS and partner solutions3. Migration Hub also supports AWS Application Discovery Service, which is a service that helps systems integrators quickly and reliably plan application migration projects by automatically identifying applications running in on-premises data centers, their associated dependencies, and their performance profile4.

https://aws.amazon.com/migration-evaluator/

https://docs.aws.amazon.com/migration-evaluator/latest/userguide/what-is.html

https://aws.amazon.com/migration-hub/

https://aws.amazon.com/application-discovery/

https://aws.amazon.com/server-migration-service/

https://aws.amazon.com/dms/

https://docs.aws.amazon.com/controltower/latest/userguide/what-is-control-tower.html

https://aws.amazon.com/application-migration-service/

https://aws.amazon.com/storagegateway/

The best solution is to deploy two firewall appliances into the shared services VPC, each in a separate Availability Zone, and create a new Gateway Load Balancer to distribute traffic to them. A Gateway Load Balancer is designed for high performance and high availability scenarios with third-party network virtual appliances, such as firewalls. It operates at the network layer and maintains flow stickiness and symmetry to a specific appliance instance. It also uses the GENEVE protocol to encapsulate traffic between the load balancer and the appliances. To route traffic from other VPCs to the Gateway Load Balancer, a VPC Gateway Load Balancer endpoint is needed. This is a VPC endpoint that provides private connectivity between the appliances in the shared services VPC and the application servers in other VPCs. The endpoint must be added as the next hop in the route table for the shared services VPC. This solution ensures reliability and minimizes failover time between firewall appliances within a single AWS Region. References: What is a Gateway Load Balancer?, Gateway load balancer - Azure Load Balancer, Introducing Azure Gateway Load Balancer: Deploy and scale network …

it covers all the requirements mentioned in the question, it will allow collecting the detailed metrics, including process information and it provides a way to query and analyze the data using Amazon Athena.

Implementing a disaster recovery strategy that minimizes data loss and addresses performance issues involves creating a read replica of the RDS DB instance in a separate region and directing the finance team ' s queries to this replica. This solution alleviates the performance impact on the primary database. Using Amazon S3 Cross-Region Replication (CRR) ensures that processed documents are available in the disaster recovery region. In the event of a disaster, the read replica can be promoted to a standalone DB instance, and EC2 instances can be launched from pre-created AMIs to serve the web frontend, thereby ensuring resiliency and minimal data loss.

AWS Documentation on Amazon RDS Read Replicas, Amazon S3 Cross-Region Replication, and Amazon EC2 AMIs provides comprehensive guidance on implementing a robust disaster recovery solution. This approach is in line with AWS best practices for high availability and disaster recovery planning.

Create a Transit Gateway:

Step 1: In the AWS Management Console, navigate to the VPC Dashboard.

Step 2: Select " Transit Gateways " and click on " Create Transit Gateway " .

Step 3: Configure the transit gateway by providing a name and setting the options for Amazon side ASN and VPN ECMP support as needed.

Step 4: Attach each of the company’s VPCs and relevant subnets to the transit gateway. This centralizes the network management and simplifies the routing configurations, supporting scalable and flexible network architecture.

Set Up AWS PrivateLink:

Step 1: Create a Network Load Balancer (NLB) in the management VPC that points to the compute resource responsible for license validation.

Step 2: Create an AWS PrivateLink endpoint service pointing to this NLB.

Step 3: Allow each customer ' s VPC to create an interface endpoint to this PrivateLink service. This setup enables secure and private communication between the customer VPCs and the management VPC, ensuring one-way access from each customer ' s VPC to the management VPC for license validation.

This combination leverages the benefits of AWS Transit Gateway for scalable and centralized routing, and AWS PrivateLink for secure and private service access, meeting the requirement with minimal operational overhead.

References

Amazon VPC-to-Amazon VPC Connectivity Options

AWS PrivateLink - Building a Scalable and Secure Multi-VPC AWS Network Infrastructure

Connecting Your VPC to Other VPCs and Networks Using a Transit Gateway

The correct approach involves activating the BusinessUnit cost allocation tag in the management account to ensure costs are tagged consistently across the organization.

The AWS Cost and Usage Report (CUR), delivered to a single Amazon S3 bucket in the management account, consolidates cost and usage data across all member accounts.

Amazon Athena can efficiently query the CUR data directly from S3, while Amazon QuickSight can be used to create interactive dashboards and reports that visualize the cost allocation by business unit.

This solution minimizes complexity, centralizes reporting, and leverages AWS native services for the best cost optimization and insights.

Comprehensive and Detailed Explanation:

Why C is the best answer

This question requires all of the following:

Bidirectional access between two separate AWS Organizations

Access to S3 buckets across accounts and organizational boundaries

Encryption at rest

Logging of all S3 bucket access

Buckets exist in different Regions

Option C is the best match because S3 Access Points are purpose-built to simplify and scale access management for shared S3 datasets. For cross-account or multi-team sharing scenarios, S3 Access Points let you define dedicated access paths and policies instead of overloading a single bucket policy. For encryption at rest, customer managed AWS KMS keys are the strongest fit when cross-account access must be explicitly controlled. For logging bucket-level object access, AWS CloudTrail is the correct service because S3 data events record object-level API activity such as GetObject and PutObject.

Why the other options are not correct

A. S3 Access Grants is not the best fit here

S3 Access Grants is designed primarily to grant S3 access to identities mapped from identity sources and simplify data access at scale. It is not the standard primary design choice for bidirectional bucket sharing between two separate organizations. The exam-focused architectural pattern for controlled cross-account S3 sharing is S3 Access Points plus policies, combined with KMS permissions and CloudTrail data events. Even though SSE-KMS and CloudTrail data events are good parts of the design, the access mechanism in A is not the best answer.

B. VPC networking services do not solve the main requirement

VPC peering, PrivateLink, and endpoint access controls are network connectivity controls. Amazon S3 is not shared between organizations by building VPC peering between their VPCs. Also, VPC Flow Logs record network flow metadata, not S3 object access. That means B fails the logging requirement and uses the wrong control plane for the access-sharing requirement.

D. Replication and SCPs do not provide bidirectional shared access

S3 Cross-Region Replication copies objects; it does not establish bidirectional shared access for both organizations to each other’s buckets. Service Control Policies also do not work the way this option describes. SCPs are guardrails within an AWS Organization; they are not shared between separate organizations to grant object access. In addition, S3 server access logging is not the best answer when the requirement is to collect logs of all S3 bucket access in an auditable way for object-level API actions. CloudTrail data events are the stronger and more exam-aligned choice for logging object access activity.

Key technical reasoning

Cross-account / cross-organization S3 access:

S3 Access Points are commonly used to provide distinct permissions and simplified policy management for different consumers of the same bucket. This is especially useful in multi-account environments.

Encryption at rest:

When access spans accounts or organizations, customer managed KMS keys are preferred because key policies and grants can explicitly authorize external principals. This gives finer control than SSE-S3 when cross-account governance matters.

Logging:

For S3 object-level operations, CloudTrail data events are the standard answer in AWS architecture questions. They record API activity such as GetObject, PutObject, and DeleteObject. This is more appropriate than VPC Flow Logs or relying only on S3 server access logging.

Regional aspect:

The fact that buckets are in us-east-1 and us-west-1 does not prevent cross-account access. S3 access control, KMS permissions, and CloudTrail can be configured in the relevant Regions.

The correct answers are A and C. Cross-Region Replication requires an S3 replication rule from the source bucket to the destination bucket, and S3 Versioning must be enabled, which the question already states. Because the business requires availability in the second Region within 15 minutes, ordinary replication is not sufficient by itself. S3 Replication Time Control is designed for this type of compliance requirement and provides replication metrics and event notifications. AWS documentation specifically identifies OperationMissedThreshold as the event that notifies the bucket owner when an object eligible for S3 RTC replication exceeds the 15-minute threshold. DataSync is not the best fit for continuous object replication with a 15-minute compliance notification requirement. Transfer Acceleration improves upload performance to S3, not replication SLA tracking. (AWS Documentation)

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it will use the ALB to handle the unpredictable bursts of traffic and route it to the SQS queue. The SQS queue will act as a buffer to store incoming data temporarily and the container running in Amazon ECS with the Fargate launch type will process messages in the queue. This approach will ensure that all data is processed and prevent data loss.

The company wants three things: minimize client-to-broker latency within the same Availability Zone, increase available bandwidth, and increase the scaling speed of the MSK cluster. The current brokers are Standard brokers (two per AZ). Clients use default settings, which means they are not explicitly configured for rack awareness or AZ affinity.

A common way to reduce latency in multi-AZ Kafka deployments is to enable rack awareness on clients and brokers so clients prefer brokers in the same “rack,” which can map to an Availability Zone. In Kafka, the client.rack setting allows the client to include rack information so the broker can return metadata that helps the client select replicas that are closest, reducing cross-AZ traffic and improving latency.

To increase bandwidth and improve scaling speed, the most direct approach in the choices is to move from Standard brokers to Express brokers. Express brokers are designed to provide higher throughput and faster scaling characteristics compared to standard broker types. Since the question explicitly calls out increasing available bandwidth and scaling speed, the broker type change is the key lever, and it can be combined with client.rack configuration to minimize cross-AZ latency.

Option C matches these requirements: it replaces Standard brokers with Express brokers (to improve throughput/bandwidth and scaling speed) and sets client.rack to the Availability Zone identifier (az_id) to improve locality and reduce latency between clients and brokers in the same AZ.

Option A is not appropriate because MSK does not use EC2 Auto Scaling predictive scaling in that manner, and Kafka clients/brokers are not “associated” with an EC2 placement group as a primary latency solution in MSK. Placement groups are for EC2 instance placement; MSK broker placement is managed by the service.

Option B introduces a proxy layer and MSK Connect in a way that increases complexity and does not directly guarantee lower latency or higher bandwidth. MSK Connect is for Kafka Connect workloads, not as a general-purpose low-latency routing proxy for Kafka clients. Cruise Control is used for partition rebalancing and cluster optimization, but it does not replace the benefits of higher-throughput broker types and client rack awareness for AZ locality.

Option D increases broker size and introduces PrivateLink endpoints. PrivateLink is about private connectivity from VPCs to services and does not inherently ensure AZ-local broker selection or reduce latency between clients and brokers in the same AZ. Also, resizing to xlarge increases capacity but does not address scaling speed and locality as directly as express brokers plus rack configuration.

Therefore, option C best meets all requirements.

D is correct because this solution modernizes the application into aserverless architectureusing API Gateway and Lambda for scalable microservices.Aurora Serverless v2supports SQL workloads and auto-scales based on demand.AWS SCTallows migration of SQL Server stored procedures to Aurora-compatible formats. This setup ensures cost efficiency, scalability, and minimal manual intervention.

A rehosts but doesn’t refactor into microservices.

B uses MySQL, which might not support the SQL Server-specific stored procedures fully.

C adds unnecessary complexity and loses relational database functionality by using DAX.

A. Modify the application deployment by building a Docker image that contains the application code. Publish the image to Amazon Elastic Container Registry (Amazon ECR). - This step is necessary to package the application code in a container and make it available for running on ECS. B. Create a new Amazon Elastic Container Service (Amazon ECS) task definition with a compatibility type of AWS Fargate. Configure the task definition to use the new image in Amazon Elastic Container Registry (Amazon ECR). Adjust the Lambda function to invoke an ECS task by using the ECS taskdefinition when a new file arrives in Amazon S3.

The current Redshift configuration uses eight ra3.4xlarge nodes with on-demand pricing and is paused outside an 8-hour daily window. The ingestion workload runs only 3 hours per day and has about 85% CPU utilization during ingestion, which indicates that the existing node size and count are adequate for performance. The main opportunity is cost optimization for a workload that is time-bound and does not require a continuously running provisioned cluster.

Amazon Redshift Serverless is designed for such intermittent or variable workloads. With Redshift Serverless, you configure compute capacity in terms of Redshift Processing Units (RPUs) and pay per use based on the RPU-hours consumed when queries and data ingestion jobs are actually running. When there is no activity, there is no compute charge. Redshift Serverless can also be created from an existing provisioned Redshift snapshot, allowing an easy migration path from a node-based cluster to a serverless endpoint without re-ingesting or manually moving data.

Option C creates a new Redshift Serverless endpoint with 64 RPUs from the most recent snapshot. This takes advantage of snapshots that already exist on the current cluster. The internal applications are then updated to read from the new serverless endpoint, and the existing cluster is deleted to stop incurring node-based charges. For a workload that runs only a few hours per day, moving to serverless compute is usually more cost-effective than maintaining a provisioned cluster, even if that cluster is paused outside the ingestion window, because serverless eliminates the need to manage cluster sizing and pausing and optimizes billing around actual usage.

Option A proposes staying on provisioned RA3 nodes and additionally purchasing 1-year Reserved Nodes. Reserved Nodes apply discount to node hours whether or not the cluster is paused or fully utilized, and they are designed to be cost-effective for steady, predictable, long-running workloads. For an 8-hour-per-day workload, especially with only 3 hours of intensive ingestion, Reserved Nodes are likely to be less cost-optimal than an on-demand, usage-based serverless model. Also, concurrency scaling is primarily for handling bursty query concurrency, not for cost reduction of a regular ingestion workload.

Option B replaces the existing eight ra3.4xlarge nodes with six ra3.16xlarge nodes and enables auto scaling. ra3.16xlarge provides significantly more capacity per node and is generally more expensive. The original workload already runs at acceptable utilization (about 85% during ingestion), so scaling up to larger nodes is not necessary for performance. Adding auto scaling on top of this likely increases costs and complexity without a clear benefit for a predictable, time-bound daily ingestion pattern.

Option D uses Redshift Spectrum and unloads data from the cluster to Amazon S3 for querying. Redshift Spectrum allows querying data stored in S3 using external tables from within a Redshift cluster, and is charged per amount of data scanned. However, the option still depends on maintaining a Redshift cluster and does not fundamentally optimize the compute cost of the ingestion workload. It also changes query patterns and data access architecture for the internal applications and may increase query cost if large volumes of S3 data are scanned by Spectrum.

Therefore, using Redshift Serverless created from the existing snapshot and paying only for compute usage during ingestion and queries, as described in option C, is the best way to optimize cost for this specific workload pattern.

The best migration strategy to meet the requirement of minimizing database service interruption before the DNS cutover is to use AWS DMS to migrate data between the two Aurora DB clusters. AWS DMS can perform continuous replication of data with high availability and consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift and Amazon S31. AWS DMS supports homogeneous migrations such as migrating from one Aurora MySQL DB cluster to another, as well as heterogeneous migrations between different database platforms2. AWS DMS also supports cross-account migrations, as long as the source and target databases are in the same AWS Region3.

The other options are not optimal for the following reasons:

Option A: Taking a snapshot of the existing Aurora database and restoring it in the new account would require a downtime during the snapshot and restore process, which could be significant for large databases. Moreover, any changes made to the source database after the snapshot would not be replicated to the target database, resulting in data inconsistency4.

Option C: Using AWS Backup to share an Aurora database backup from the existing AWS account to the new AWS account would have the same drawbacks as option A, as AWS Backup uses snapshots to create backups of Aurora databases.

Option D: Using AWS Application Migration Service to migrate data between the two Aurora DB clusters is not a valid option, as AWS Application Migration Service is designed to migrate applications, not databases, to AWS. AWS Application Migration Service can migrate applications from on-premises or other cloud environments to AWS, using agentless or agent-based methods.

Creating an AWS Cost and Usage Report for the organization and defining tags and cost categories in the report will allow for detailed cost reporting for the different companies that have been consolidated into one organization. By creating a table in Amazon Athena and an Amazon QuickSight dataset based on the Athena table, the finance team will be able to easily query and generate reports on the costs for all the companies. The dataset can then be shared with the finance team for them to use for their reporting needs.

https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-vpc-endpoint-policies.html

This option uses different types of savings plans and reserved nodes to minimize the company’s overall monthly costs for running its application on EC2 instances, Lambda functions, and MemoryDB cache nodes. Savings plans are flexible pricing models that offer significant savings on AWS usage (up to 72%) in exchange for a commitment of a consistent amount of usage (measured in $/hour) for a one-year or three-year term. There are two types of savings plans: Compute Savings Plans and EC2 Instance Savings Plans. Compute Savings Plans apply to any compute usage across EC2 instances, Fargate containers, Lambda functions, SageMaker notebooks, and ECS tasks. EC2 Instance Savings Plans apply to a specific instance family within a region and provide more savings than Compute Savings Plans (up to 66% versus up to 54%). Reserved nodes are similar to savings plans but apply only to MemoryDB cache nodes. They offer up to 55% savings compared to on-demand pricing.

 AWS Trusted Advisor is a service that provides real-time guidance to help users provision their resources following AWS best practices1. One of the Trusted Advisor checks is “Low Utilization Amazon EC2 Instances”, which identifies EC2 instances that appear to be underutilized based on CPU, network I/O, and disk I/O metrics1. This check can help users optimize the cost and size of their EC2 instances by recommending smaller or more appropriate instance types.

AWS Compute Optimizer is a service that analyzes the configuration and utilization metrics of AWS resources and generates optimization recommendations to reduce the costand improve the performance of workloads2. Compute Optimizer supports four types of AWS resources: EC2 instances, EBS volumes, ECS services on AWSFargate, and Lambdafunctions2. For EC2 instances, Compute Optimizer evaluates the vCPUs, memory, storage, and other specifications, as well as the CPU utilization, network in and out, disk read and write, and other utilization metrics of currently running instances3. It then recommends optimal instance types based on price-performance trade-offs.

Option A is incorrect because purchasing AWS Business Support or AWS Enterprise Support for the account will not directly help with cost-optimization and sizing of EC2 instances. However, these support plans do provide access to more Trusted Advisor checks than the basic support plan1.

Option C is incorrect because installing the Amazon CloudWatch agent and configuring memory metric collection on the EC2 instances will not provide any optimization recommendations by itself. However, memory metrics can be used by Compute Optimizer to enhance its recommendations if enabled3.

Option E is incorrect because creating an EC2 Instance Savings Plan for the AWS Regions, instance families, and operating systems of interest will not help with cost-optimization and sizing of EC2 instances. Savings Plans are a flexible pricing model that offer lower prices on Amazon EC2 usage in exchange for a commitment to a consistent amount of usage for a 1- or 3-year term4. Savings Plans do not affect the configuration or utilization of EC2 instances.

The best solution is to create an FSx for Windows File Server file system in the DR Region and establish connectivity between the VPCs in both Regions by using VPC peering. This will ensure that the data does not travel across the public internet and meets the compliance requirements. By using AWS DataSync with interface VPC endpoints and AWS PrivateLink, the data can be copied securely and efficiently between the FSx for Windows File Server file systems in both Regions. This solution also provides the ability to fail over to the DR Region in case of a disaster. References: [Amazon FSx for Windows File Server User Guide], [AWS DataSync User Guide] , [Amazon VPC User Guide]

On-demand capacity mode is the function of Dynamodb.https://aws.amazon.com/blogs/news/running-spiky-workloads-and-optimizing-costs-by-more-than-90-using-amazon-dynamodb-on-demand-capacity-mode/

Amazon DocumentDB Elastic Clustershttps://aws.amazon.com/blogs/news/announcing-amazon-documentdb-elastic-clusters/

Deploy Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones for the Java backend application. This will provide high availability and scalability, while allowing the company to retain the same database structure as the original application.

Create VPC Endpoint Service:

In the shared VPC, create a VPC endpoint service using the Network Load Balancer (NLB) that fronts the centralized application.

Enable the option to require endpoint acceptance to control which business unit VPCs can connect to the service.

Set Up VPC Endpoints in Business Unit VPCs:

In each business unit VPC, create a VPC endpoint that points to the VPC endpoint service created in the shared VPC.

Use the service name of the endpoint service created in the shared VPC for configuration.

Accept Endpoint Requests:

From the VPC endpoint service console in the shared VPC, review and accept endpoint connection requests from authorized business unit VPCs. This ensures that only authorized VPCs can access the centralized application.

Configure Routing:

Update the route tables in each business unit VPC to direct traffic destined for the centralized application through the VPC endpoint.

This solution ensures secure, private connectivity between the business unit VPCs and the shared VPC, even if there are overlapping CIDR blocks. It leverages AWS PrivateLink and VPC endpoints to provide scalable and controlled access​(AWS Documentation)​​(Amazon Web Services, Inc.)​.

This option allows the solution to decouple the API from the Lambda function and use EventBridge as an event-driven service that can handle failures gracefully1. By using two event buses, one for normal events and one for failed events, the solution can ensure that no data is lost and that data can be processed later if failures occur2. The primary event bus receives the data from the weather stations through the API integration and triggers the Lambda function through a rule. The Lambda function can then call the third-party service for data pre-processing. If the third-party service fails, the Lambda function can send an error response to EventBridge, which will route it to the secondary event bus as a failure destination3. The secondary event bus can then store the failed events in another service, such as Amazon S3 or Amazon SQS, for troubleshooting or reprocessing.

Using Amazon EventBridge with AWS Lambda

Using multiple event buses

Using failure destinations

[Using dead-letter queues]

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

The correct answer is A, D, and F. Existing AWS accounts can be invited into an AWS Organizations organization, and SCPs are the correct organization-level mechanism to enforce preventive controls such as required tagging. Because OUs contain accounts, not individual EC2 instances, development workloads must be recreated or migrated into an account that is placed under the development OU. Amazon Inspector is the correct service for automated EC2 vulnerability scanning, and Security Hub can aggregate and centralize findings across an organization. For patching, AWS Systems Manager Patch Manager can be managed across an organization through delegated administration and organization-wide patch policies, which reduces operational overhead compared with configuring each account separately. Trusted Advisor is not the right vulnerability scanning service, and configuring Patch Manager separately in every account creates unnecessary administration.

https://repost.aws/knowledge-center/vpc-ip-address-range

Set Up EventBridge Event Bus:

Step 1: Open the Amazon EventBridge console and create a custom event bus. This bus will be used to handle user deletion events.

Step 2: Name the event bus appropriately (e.g.,user-deletion-bus).

Post Events on User Deletion:

Step 1: Modify the central user service to post an event to the custom EventBridge event bus whenever a user is deleted.

Step 2: Ensure the event includes relevant details such as the user ID and any other necessary metadata.

Create EventBridge Rules for Microservices:

Step 1: For each microservice that needs to delete user data, create a new rule in EventBridge that triggers on the user deletion event.

Step 2: Define the event pattern to match the user deletion event. This pattern should include the event details posted by the central user service.

Invoke Microservice Logic:

Step 1: Configure the EventBridge rule to invoke a target, such as an AWS Lambda function, which contains the logic to delete the user data from the microservice ' s data store.

Step 2: Each microservice should have its Lambda function or equivalent logic to handle the deletion of user data upon receiving the event.

Using Amazon EventBridge ensures a scalable, reliable, and decoupled approach to handle the deletion of user data across multiple microservices. This setup allows each microservice to independently process user deletion events without direct dependencies on other services.

References

AWS EventBridge Documentation

DynamoDB Streams and AWS Lambda Triggers

Implementing the Transactional Outbox Pattern with EventBridge Pipes​(AWS Documentation)​​(Amazon Web Services, Inc.)​​(Amazon Web Services, Inc.)​​(AWS Documentation)​​(AWS Cloud Community)​.

According to the AWS documentation1, AWS App2Container (A2C) is a command line tool for migrating and modernizing Java and .NET web applications into container format. AWS A2C analyzes and builds an inventory of applications running in bare metal, virtual machines, Amazon Elastic Compute Cloud (EC2) instances, or in the cloud. You can use AWS A2C to generate container images for your applications and deploy them on Amazon ECS or Amazon EKS.

Option A meets the requirements of the scenario because it allows you to migrate your existing Java application to AWS and minimize the administrative overhead to maintain the servers. You can use AWS A2C to analyze your application dependencies, extract application artifacts, and generate a Dockerfile. You can then store your container images in Amazon ECR, which is a fully managed container registry service. You can use AWS Fargate as the launch type for your Amazon ECS cluster, which is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can grant the ECS task execution role permission to access the ECR image repository, which allows your tasks to pull images from ECR. You can configure Amazon ECS to use an ALB, whichis a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use the ALB to interact with your application.

Dis correct:Aurora PostgreSQLis afully managed, high-availabilitydatabase engine.

UseAWS DMSto migrate data andAWS Schema Conversion Tool (SCT)to convert stored procedures from Oracle to Aurora-compatible code.

This meets the requirements ofminimal downtime,managed services, andstored procedure support.

Ais not valid—RDS doesn’t allow custom S3 integration for storage.

Binvolves EC2, which violates the “managed services” constraint.

Crequires complete rewrite of the application logic, which contradicts the minimal downtime requirement.

This option allows the company to use Amazon Aurora MySQL, which is a fully managed relational database service that is compatible with MySQL and offers up to five times better performance than standard MySQL1. By migrating the database to Aurora MySQL, the company can benefitfrom its high availability, durability, scalability, and security features1. By deploying the application in an Auto Scaling group on Amazon EC2 instances behind an Application LoadBalancer, the company can ensure that the application can handle varying levels of traffic and distribute the requests across multiple instances2. By storing sessions in an Amazon ElastiCache for Redis replication group, the company can improve the performance and reliability of the session data by using a fast, in-memory data store that supports replication and failover3.

What is Amazon Aurora?

What is Auto Scaling?

What is Amazon ElastiCache?

A: To run on Graviton, containers must supportARM64.

C: Graviton-based EC2 instances offer significant cost savings and better price-performance.

E: Once migrated, aSavings Planfor the new instance family ensures additional cost optimization.

B is a non-sensical option.

D and F continue with x86, which is more expensive.

https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpoints-access.html

https://aws.amazon.com/premiumsupport/knowledge-center/vpc-reduce-nat-gateway-transfer-costs/

VPC endpoint policies enable you to control access by either attaching a policy to a VPC endpoint or by using additional fields in a policy that is attached to an IAM user, group, or role to restrict access to only occur via the specified VPC endpoint

Amazon Simple Notification Service (Amazon SNS) is a fully managed pub/sub messaging service that enables users to send messages to multiple subscribers1. Users can sendevent details to an Amazon SNS topic and configure an AWS Lambda function as a subscriber to the SNS topic to process the events. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. Users can add an on-failure destination to the function and set an Amazon Simple Queue Service (Amazon SQS) queue as the target. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications3. This way, if a processing error occurs, the event will move into the separate queue for review.

Option B is incorrect because publishing events to an Amazon SQS queue and creating an Amazon EC2 Auto Scaling group will not have the ability to scale in and out based on the number of events that the solution receives. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud. Auto Scaling is a feature that helps users maintain application availability and allows them to scale their EC2 capacity up or down automatically according to conditions they define. However, for this use case, using SQS and EC2 will not take advantage of the serverless capabilities of Lambda and SNS.

Option C is incorrect because writing events to an Amazon DynamoDB table and configuring a DynamoDB stream for the table will not have the ability to move events into a separate queue for review if a processing error occurs. Amazon DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB Streams is a feature that captures data modification events in DynamoDB tables. Users can configure the stream to invoke a Lambda function, but they cannot configure an on-failure destination for the function.

Option D is incorrect because publishing events to an Amazon EventBridge event bus and setting an Application Load Balancer (ALB) as the event bus target will not have the ability to move events into a separate queue for review if a processing error occurs. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. An ALB is a load balancer that distributes incoming application traffic across multiple targets, such as EC2 instances, containers, IP addresses, Lambda functions, and virtual appliances. Users can configure EventBridge to retry events, but they cannot configure an on-failure destination for the ALB.

 This solution will provide the highest availability for the database, as the read replicas will allow the database to be available in multiple Regions, thus reducing the chances of disruption. Additionally, the promotion of the cross-Region read replica to become a standalone DB instance will ensure that the database is still available even if one of the Regions experiences disruptions.

AWS Global Accelerator is a networking service that helps you improve the availability and performance of the applications that you offer to your global users1. It provides static IP addresses that act as a fixed entry point to your applications and route user traffic to the optimal endpoint based on performance, health, and policies that you configure1. By creating a standard accelerator endpoint for the NLB in each additional Region, you can ensure that customers are automatically directed to the Region that is geographically closest to them2. You can also provide customers with the Global Accelerator IP address, which is anycast from AWS edge locations and does not change when you add or remove endpoints3.

What is AWS Global Accelerator?

Standard accelerator endpoints

AWS Global Accelerator IP addresses

The best solution is to create an AWS WAF web ACL and configure a rule to block any requests that do not originate from the specified country. This will ensure that only users from the allowed country can access the application. AWS WAF also provides logging capabilities that can capture the access requests that have been blocked. This solution requires the least possible maintenance as it does not involve updating IP ranges or security group rules. References: [AWS WAF Developer Guide], [AWS Shield Developer Guide]

To develop a migration plan with accurate inventory and dependency data:

AWS Migration Hub provides a single view for tracking migration tasks and resources across multiple AWS services.

The AWS Application Discovery Agent (installed on servers) collects detailed data about running processes, system performance, and network connections.

Migration Hub Strategy Recommendations leverages this data to automatically identify application patterns, generate recommended AWS target services, and provide a detailed migration plan.This approach ensures accurate data collection, detailed dependency mapping, and tailored recommendations—crucial for a successful and right-sized migration to AWS.

This solution will give the Lambda function access to the internet by routing its outbound traffic through the NAT gateway, which has a public Elastic IP address. This will allow the external provider to whitelist the single public IP address associated with the NAT gateway, and enable the application to access the new service

Deploying a NAT gateway and associating an Elastic IP address with it, and then configuring the VPC to use the NAT gateway, will give the application the ability to access the new service. This is because the NAT gateway will be the single public IP address that the external provider needs for the allow list. The NAT gateway will allow the application to access the service, while keeping the underlying Lambda functions private.

When configuring NAT gateways, you should ensure that the route table associated with the NAT gateway has a route to the internet gateway with a target of the internet gateway. Additionally, youshould ensure that the security group associated with the NAT gateway allows outbound traffic from the Lambda functions.

The Auto Scaling group can automatically launch and terminate EC2 instances based on the demand and health of the web application. The launch template can specify the configuration of the EC2 instances, such as the AMI, instance type, security group, and user data. The existing ALB can distribute the traffic to the EC2 instances in the Auto Scaling group. The existing EC2 instances can be attached to the Auto Scaling group without deleting them or the ALB. This option minimizes downtime and preserves the current setup of the web application. References: [What is Amazon EC2 Auto Scaling?], [Launch templates] , [Attach a load balancer to your Auto Scaling group], [Attach EC2 instances to your Auto Scaling group]

By splitting the 12 instances across three Availability Zones, the system can maintain high availability and availability of resources in case of a failure. Option D also uses a combination of On-Demand Instances with Capacity Reservations and Spot Instances, which allows for scheduled jobs to be run on the On-Demand instances with guaranteed capacity, while also taking advantage of the cost savings from Spot Instances for the user jobs which have lower SLA requirements.

AWS Cost Anomaly Detection is a feature of the AWS Cost Management suite that leverages machine learning to enable continuous monitoring of your AWS costs and usage, allowing you to identify unexpected and abnormal spending1. You can create cost monitors that evaluate specific AWS services, member accounts, cost allocation tags, orcost categories based on your AWS account structure2. You can also configure alert subscriptions that notify you when a cost monitor detects an anomaly that meets your threshold2. In this case, you can create a cost monitor with a monitor type of AWS Service and apply a filter of Amazon EC2 to track the EC2 usage as a metric. You can then configure an alert subscription to notify the architecture team if the usage is 10% more than the average usage for the last 30 days, which is the anomaly detection period used by AWS Cost Anomaly Detection3.

https://aws.amazon.com/solutions/implementations/disaster-recovery-for-aws-iot/

https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage_lens.html

Creating a headless Aurora DB cluster in the secondary Region as part of an Aurora global database ensures continuous, low-latency replication without running active instances in the secondary Region.

This configuration meets the RTO and RPO requirements by allowing rapid promotion of the secondary cluster in a failover scenario.

It also reduces operational costs compared to continuously running standby instances in the secondary Region.

This aligns with AWS best practices for DR and cross-Region resiliency.

because Amazon CloudFront considers the case of parameter names and values when caching based on query string parameters , thus inconsistent query strings may cause CloudFront to forward mixed-cased/misordered requests to the origin. Triggering a Lambda@Edge function based on a viewer request event to sort parameters by name and force them to be lowercase is the best choice. https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/QueryStringParameters.html#query-string-parameters-optimizing-cachinghttps://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-cloudfront-trigger-events.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-examples.html#lambda-examples-normalize-query-string-parameters

(https://docs.aws.amazon.com/controltower/latest/userguide/strongly-recommended-guardrails.html)

https://aws.amazon.com/kinesis/data-firehose/faqs/

The data scientists’ EC2 instances in a separate account must access S3 data in a controlled way that satisfies the policy: only authorized networks may access the IoT data. “Authorized networks” in this context means traffic must originate from approved VPCs and must not traverse the public internet.

First, traffic from the EC2 instances to S3 must stay on the AWS network without using public endpoints. A gateway VPC endpoint for S3 in the data scientists’ VPC (option A) allows EC2 instances in that VPC to reach S3 over private connectivity. When using a gateway VPC endpoint, the route tables of the subnets associated with the endpoint automatically route S3 traffic through the endpoint. This ensures that access to S3 is from an authorized network (the VPC) instead of from the public internet.

Second, access must be constrained such that only calls made through the intended S3 access mechanism are allowed. The data lake bucket already has an S3 access point. S3 access points can be restricted by policy and can be referenced in bucket policies through the s3:DataAccessPointArn condition key. By using an S3 bucket policy that allows s3:GetObject only when s3:DataAccessPointArn matches the expected access point ARN (option E), the company can enforce that all valid access uses the approved access point configuration. Combined with the access point’s own network configuration and the VPC endpoint, access is limited to authorized networks.

Option B, blocking public access on the access point, is a good general security practice but does not by itself guarantee that access is restricted to authorized VPC networks. The main enforcement must be through VPC endpoints and bucket/access point policies.

Option C denies s3:GetObject when s3:DataAccessPointArn is a valid access point ARN, which is the opposite of what is required. This would prevent intended access via the access point rather than allow it.

Option D is not correct because gateway VPC endpoints for S3 are configured via endpoint associations with route tables, not by directly routing to an access point in the route table. Traffic to S3 is routed to the VPC endpoint, and the endpoint plus S3 policies determine access.

Therefore, creating a gateway VPC endpoint for S3 in the data scientists’ VPC (option A) and using a bucket policy condition on s3:DataAccessPointArn to allow access only through the authorized access point (option E) together meet the requirement of secure, network-restricted access from authorized networks.

The workload is containerized, runs for hours, and is event-driven by nightly data arrival in Amazon S3. The current architecture uses EC2 instances and cron jobs, which results in operational overhead (managing instances, patching, scaling, scheduling) and idle compute between processing windows.

A key constraint is that the processing tasks can take hours. AWS Lambda has maximum execution duration limits that make it unsuitable for multi-hour batch processing. Even though Lambda can run container images, it still must complete within Lambda’s runtime limit. Packaging container images as Lambda layers is also not an appropriate pattern for long-running container workloads and adds complexity.

A modern, low-ops approach for long-running, containerized batch jobs is to run containers on AWS Fargate. Fargate removes the need to manage EC2 instances and allows tasks to run for extended periods as needed, scaling based on demand. Because the workload is composed of several data-processing services that likely need orchestration (for example, fan-out, sequencing, retries, parallelism), AWS Step Functions is well suited to coordinate the workflow and invoke the appropriate ECS tasks.

For triggering based on new S3 data, Amazon EventBridge provides a managed, scalable event bus for AWS service events, including S3 object events, and can route events to targets such as Step Functions state machines. Using EventBridge reduces the need for direct point-to-point notification wiring and provides centralized event routing, filtering, and monitoring.

Option C combines all the right elements: it runs the containers as ECS tasks on Fargate to eliminate EC2 management and idle capacity, uses Step Functions to orchestrate tasks that can run for hours, and uses EventBridge to trigger the state machine when new data is uploaded to S3. This replaces the per-instance cron scheduling with an event-driven serverless orchestration model and significantly reduces operational overhead.

Option B is close but is less appropriate as written because S3 Event Notifications are typically configured to send to Amazon SQS, Amazon SNS, or AWS Lambda. Triggering Step Functions directly is more naturally handled through EventBridge rules. EventBridge is also the recommended event routing layer for integrating service events into workflows.

Option A is not suitable because Lambda is not designed for multi-hour processing jobs due to runtime limits.

Option D is incorrect because Lambda layers are for sharing libraries and runtime dependencies, not for packaging multi-hour container workloads. It also still depends on Lambda runtime limits and does not match the operational model for long-running batch processing.

Therefore, option C is the best modernization approach with the least operational overhead.

https://aws.amazon.com/getting-started/hands-on/move-to-managed/migrate-mongodb-to-documentdb/

The correct answer is A, C, and F. The company wants to host a private DNS namespace, myvpc.example.com, inside AWS and allow on-premises devices to resolve records in that namespace. A Route 53 private hosted zone is the correct hosted zone type because the domain is intended for private VPC resolution, not public internet DNS. To allow DNS queries from the on-premises network into AWS, Route 53 Resolver inbound endpoints are required. The on-premises DNS resolvers then need conditional forwarding rules that forward queries for myvpc.example.com to the IP addresses of the inbound Resolver endpoint. An outbound endpoint is used when AWS resources need to resolve DNS names hosted on premises. That is not the stated requirement here. A public hosted zone would expose records publicly and is not appropriate for private VPC DNS.

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

This solution uses a combination of AWS Resource Access Manager (RAM) and automated backups to migrate the Lambda functions and the Aurora database to theTarget account while minimizing downtime. In this solution, the Lambda function deployment package is downloaded from the Source account and used to create new Lambda functions in the Target account. The Aurora DB cluster is shared with the Target account using AWS RAM and the Target account is granted permission to clone the Aurora DB cluster, allowing for a new copy of the Aurora database to be created in the Target account. This approach allows for the data to be migrated to the Target account while minimizing downtime, as the Target account can use the cloned Aurora database while the original Aurora database continues to be used in the Source account.

https://aws.amazon.com/blogs/mt/implement-aws-resource-tagging-strategy-using-aws-tag-policies-and-service-control-policies-scps/

Comprehensive and Detailed in Depth Explanation:

C is correct because AWS Transit Gateway is the most scalable and efficient way to interconnect hundreds of VPCs. By deploying one transit gateway per OU and sharing it with AWS RAM, each OU can isolate its network traffic and maintain internal communication without affecting or exposing other OUs.

Configure AWS Budgets in the organizationג€™s master account and configure budget alerts that are grouped by application, environment, and owner. Add each business unit to an Amazon SNS topic for each alert. Use Cost Explorer in the organizationג€™s master account to create monthly reports for each business unit.

https://aws.amazon.com/about-aws/whats-new/2019/07/introducing-aws-budgets-reports/#:~:text=AWS%20Budgets%20gives%20you%20the,below%20the%20threshold%20you%20define

(Store the uploaded images in an S3 bucket and use S3 event notification with SQS queue) is the most suitable design. Amazon S3 provides highly scalable and durable storage for theuploaded images. Configuring S3 event notifications to send messages to an SQS queue allows for decoupling the processing of images from the upload process. A fleet of EC2 instances can pull messages from the SQS queue to process the images and store them in another S3 bucket. Scaling out the EC2 instances based on SQS queue depth using CloudWatch metrics ensures efficient utilization of resources. Enabling Amazon CloudFront with the origin set to the S3 bucket containing the processed images improves the global availability and performance of image delivery.

For an EC2 instance to appear as a managed instance in AWS Systems Manager, several prerequisites must be met. Two of the most fundamental requirements are that the Systems Manager Agent (SSM Agent) is installed and running on the instance, and that the instance has an IAM role attached that grants the necessary permissions for Systems Manager to communicate with the service.

When importing a VM from an on-premises environment, the resulting AMI might not include the SSM Agent by default, or the agent might not be enabled to start automatically. If the agent is missing or not running, the instance cannot register with Systems Manager, and it will not appear in the Systems Manager console as a managed instance. Therefore, verifying that the SSM Agent is installed and running is a primary troubleshooting step.

Additionally, the EC2 instance must have an IAM instance profile (role) attached that includes the permissions required by Systems Manager. Typically, this is achieved by attaching a role that includes the AmazonSSMManagedInstanceCore managed policy. Without the appropriate IAM role, even a correctly installed and running agent will not be able to register the instance or perform Systems Manager operations.

Option C (VPC endpoint existence) is not required in this scenario because the instance is in a public subnet and has a public IP address. In this case, the instance can communicate with the Systems Manager endpoints over the internet. VPC endpoints are required when instances do not have internet access (for example, in private subnets without NAT or public IPs), which is not the case here.

Option D (Application Discovery Agent) is unrelated. The AWS Application Discovery Agent is used for migration assessment and discovery of on-premises workloads, not for Systems Manager managed instance registration.

Option E (service-linked roles) is generally not a common cause for a single instance failing to appear as managed, especially when other Systems Manager functionality is working in the account. Service-linked roles for Systems Manager are usually created automatically when needed and do not typically prevent an individual instance from registering if the agent and instance role are correctly configured.

Therefore, the correct troubleshooting steps are to verify the presence and operation of the Systems Manager Agent and to ensure the EC2 instance has the correct IAM role attached.

The workload uses EC2 Spot Instances, which are subject to capacity availability constraints. Using a single instance type significantly increases the risk of launch failures when Spot capacity for that instance type is constrained. This directly impacts application reliability and increases user wait times.

Attribute-based instance type selection allows Auto Scaling to choose from multiple compatible instance types based on defined requirements such as vCPU count, memory, and architecture. This improves the probability that Spot capacity is available at launch time because Auto Scaling can select from a broader pool of instance types instead of a single one.

Option D correctly applies attribute-based instance type selection within a new version of the existing launch template. Auto Scaling groups are designed to work with launch templates, and updating the template version is the modern and recommended approach. This change increases Spot placement flexibility and reliability without altering application behavior or introducing additional infrastructure.

Option A is incorrect because launch configurations are an older construct and do not support attribute-based instance type selection. They are also not recommended for new or updated Auto Scaling designs.

Option B does not address the root cause of Spot launch failures. Simply using a larger instance type may actually reduce available Spot capacity and worsen reliability.

Option C increases the number of placement groups, but placement groups do not solve Spot capacity shortages. They influence instance placement for networking or latency characteristics, not Spot availability across instance types.

Therefore, updating the launch template to use attribute-based instance type selection is the most effective way to improve reliability for a Spot-based Auto Scaling workload.

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_org_support-all-features.html

https://docs.aws.amazon.com/singlesignon/latest/userguide/get-started-prereqs-considerations.html

https://aws.amazon.com/about-aws/whats-new/2016/04/transfer-files-into-amazon-s3-up-to-300-percent-faster/

Option B is correct because AWS Organizations allows a company to create a new organization from a chosen payer account and define an organizational unit hierarchy. This way, the finance department can have a centralized method for payment but also maintain visibility into each group’s spending to allocate costs. The company can also invite the existing accounts to join the organization and create new accounts using Organizations, which simplifies the account management process.

Option D is correct because enabling all features of AWS Organizations and establishing appropriate service control policies (SCPs) that filter IAM permissions for sub-accounts allows the security team to have a centralized mechanism to control IAM usage in all the company’s accounts. SCPs are policies that specify the maximum permissions for an organization or organizational unit (OU), and they can be used to restrict access to certain services or actions across all accounts in an organization.

Option A is incorrect because using a collection of parameterized AWS CloudFormation templates defining common IAM permissions that are launched into each account requires more effort than using SCPs. Moreover, it does not provide a centralized mechanism to control IAM usage, as each account would have to launch the appropriate stacks to enforce the least privilege model.

Option C is incorrect because requiring each business unit to use its own AWS accounts does not provide a centralized method for payment or a centralized mechanism to control IAM usage. Tagging each AWS account appropriately and enabling Cost Explorer to administer chargebacks may help with cost allocation, but it is not as efficient as using AWS Organizations.

Option E is incorrect because consolidating all of the company’s AWS accounts into a single AWS account does not provide visibility into each group’s spending or a way to control IAM usage for different business units. Using tags for billing purposes and the IAM’s Access Advisor feature to enforce the least privilege model may help with cost optimization and security, but it is not as scalable or flexible as using AWS Organizations.

AWS Organizations

Service Control Policies

AWS CloudFormation

Cost Explorer

IAM Access Advisor

in this solution, two transit VIFs are created - one from the DX-A connection and one from the DX-B connection - into the same Direct Connect gateway for high availability. Both the eu-west-1 and us-east-1 transit gateways are then associated with this Direct Connect gateway. The transit gateways are then peered with each other to support cross-Region routing. This solution meets the requirements of the company by creating a highly available connection between the on-premises data center and the VPCs in both the eu-west-1 and us-east-1 regions, and by enabling direct traffic routing between VPCs in those regions.

The correct answer is D. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate and enable auto scaling behind an Application Load Balancer. Create additional read replicas for the DB instance. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluster. Store static content in Amazon S3 behind an Amazon CloudFront distribution.

Option D meets the requirements of the scenario because it allows you to reduce operational overhead and support the product release by using the following AWS services and features:

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed service that allows you to run Kubernetes applications on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use Amazon EKS to deploy your containerized application services on a Kubernetes cluster that is compatible with your existing tools and processes.

AWS Fargate is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can use AWS Fargate as the launch type for your Amazon EKS pods, which are the smallest deployable units of computing in Kubernetes. You can also enable auto scaling for your pods, which allows you to automatically adjust the number of pods based on the demand or custom metrics.

An Application Load Balancer (ALB) is a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use an ALB to balance the load across your Amazon EKS pods and provide high availability and fault tolerance for your application.

Amazon RDS for PostgreSQL is a fully managed relational database service that supports the PostgreSQL open source database engine. You can create additional read replicas for your DB instance, which are copies of your primary DB instance that can handle read-only queries and improve performance. You can also useread replicas to scale out beyond the capacity of a single DB instance for read-heavy workloads.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open source platform for building real-time data pipelines and streaming applications. You can use Amazon MSK to create and manage a Kafka cluster that is highly available, secure, and compatible with your existing Kafka applications. You can also configure your application services to use the Amazon MSK cluster as a source or destination of streaming data.

Amazon S3 is an object storage service that offers high durability, availability, and scalability. You can store static content such as images, videos, or documents in Amazon S3 buckets, which are containers for objects. You can also serve static content directly from Amazon S3 using public URLs or presigned URLs.

Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. You can use Amazon CloudFront to create a distribution that caches static content from your Amazon S3 bucket at edge locations closer to your users. This can improve the performance and user experience of your application.

Option A is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating additional read replicas for the DB instance would not provide high availability or fault tolerance in case of a failure of the primary DB instance, unlike deploying the DB instance in Multi-AZ mode. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK.

Option B is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK. Storing and serving static content directly from Amazon S3 would not provide optimal performance and user experience, unlike using Amazon CloudFront.

Option C is incorrect because deploying the application on a Kubernetes cluster created on the EC2 instances behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances and Kubernetes control plane for your containers. Using Amazon API Gateway to interact with the application would add an unnecessary layer of complexity and cost to your architecture, as you would need to create and maintain an API gateway that proxies requests to your ALB.

The requirement is to test IPv6 connectivity in an AWS VPC. When a VPC is associated with an IPv6 CIDR block, subnets can be configured to assign IPv6 addresses to resources, and routing must be correctly configured to allow IPv6 traffic to flow.

For public IPv6 connectivity, IPv6 traffic must be routed through an internet gateway. Unlike IPv4, IPv6 addresses are globally routable by default, and there is no concept of NAT for IPv6 egress through a NAT gateway. Therefore, for an EC2 instance in a public subnet that needs inbound and outbound IPv6 connectivity, the subnet route table must include a route that sends IPv6 traffic (::/0) to an internet gateway. Option C correctly describes this configuration and is a standard pattern for enabling IPv6 access for public subnets.

For private subnets that require outbound-only IPv6 connectivity (for example, instances that must initiate connections to the internet but must not accept inbound connections), AWS provides an egress-only internet gateway. An egress-only internet gateway allows outbound IPv6 traffic while blocking unsolicited inbound IPv6 traffic, similar in intent to how a NAT gateway is used for IPv4. Option E correctly uses an egress-only internet gateway for IPv6 traffic from a private subnet, making it the correct choice for private IPv6 connectivity testing.

Option A focuses on Direct Connect integration. Although Direct Connect can support IPv6, associating a virtual private gateway with a Direct Connect gateway is not required simply to test IPv6 connectivity to customers worldwide. This option also does not explicitly configure IPv6 internet routing and therefore does not directly meet the stated requirement.

Option B is incorrect because NAT gateways do not support IPv6. NAT gateways are IPv4-only services, so routing IPv6 traffic to a NAT gateway is not a valid configuration.

Option D is also incorrect because NAT instances do not provide a supported or recommended solution for IPv6 traffic. NAT-based designs are intended for IPv4 address translation and are not used for IPv6 connectivity in AWS.

Therefore, using an internet gateway for public IPv6 subnets and an egress-only internet gateway for private IPv6 subnets is the correct and supported approach.

By integrating a Lambda pre-provisioning hook into the AWS IoT Core provisioning template, the serial number can be validated against the CA before the device is allowed to send data.

Using allow-auto-registration ensures that only installed devices can register and send data, meeting the security requirement that sensors cannot send data until physically installed.

This approach follows AWS IoT’s secure onboarding pattern for managing device identity and authorization.

https://joe.blog.freemansoft.com/2020/04/protect-data-in-cloud-with-s3-access.html

This option will optimize the application’s performance by reducing the overhead of opening and closing database connections for each Lambda invocation. An RDS proxy is a fully managed database proxy for Amazon RDS that makes applications more scalable, more resilient to database failures, andmore secure1. It allows applications to pool and share connections established with the database, improving database efficiency and application scalability1. By creating an RDS proxy by using the Lambda console, you caneasily configure your Lambda function to use the proxy endpoint instead of the directdatabase endpoint2. This will enable your Lambda function to reuse existing connections from the proxy’s connection pool, reducing the latency and CPU utilization caused by establishing new connections for each invocation. It will also prevent connection saturation or exhaustion on the database, which can degrade performance or cause errors3.

D is correct because AWS Private CA supports resource sharing through AWS RAM, which allows you to share the CA across accounts in your AWS Organization securely. It ensures the CA private key remains secure and is never exported.

A is invalid because you cannot export a CA’s private key, and importing/exporting CAs this way is unsupported.

B is incorrect — IAM policies alone are not sufficient to share Private CAs across accounts.

C is not applicable because resource-based delegation policies do not apply to AWS Private CA.

To restrict IAM actions to only administrator roles across all AWS accounts in an organization, the most operationally efficient solution is to create a Service Control Policy (SCP) that allows IAM actions exclusively for administrator roles and apply this SCP to the root Organizational Unit (OU) of AWS Organizations. This method ensures a centralized governance mechanism that uniformly applies the policy across all accounts, thereby minimizing the need for individual account-level configurations and reducing operational complexity.

AWS Documentation on AWS Organizations and Service Control Policies offers comprehensive information on creating and managing SCPs for organizational-wide policy enforcement. This approach aligns with AWS best practices for managing permissions and ensuring secure and compliant account configurations within an AWS Organization.

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.htmlhttps://docs.aws.amazon.com/directoryservice/latest/admin-guide/ms_ad_join_instance.html

A is correct because:

DynamoDB global tablesallow for multi-Region, active-active usage.

RDS MySQL read replicain another Region supports read workloads and can bepromotedduring disaster to act as a standalone DB.

B is incorrect: DAX is a cache, not a replication mechanism.

C is wrong because Multi-AZ doesn’t span Regions.

D is more manual and error-prone.

The application is business-critical with an SLA requirement of 99.95% uptime and is currently limited by an on-premises data center. The architecture includes a PostgreSQL database and separate business logic and presentation layers. Remote users experience high latency when accessing the application, which is latency-sensitive.

To meet or exceed a 99.95% SLA for the database while minimizing operational burden, a managed database service with built-in high availability is preferred. Amazon Aurora PostgreSQL-compatible editions provide high availability and durability by automatically replicating data across multiple Availability Zones in a Region. Aurora can offer higher availability and faster failover compared to self-managed databases on EC2, and offloads patching, backups, and maintenance.

For the application and presentation layers, running them on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer preserves the basic architecture (stateless application servers behind a load balancer) while moving to a managed, highly available environment that can scale out and in automatically based on demand. This results in minimal changes to the application components and provides improved resilience compared to on-premises VMs.

Remote users experience slow load times because their desktop clients connect over higher-latency networks to the on-premises servers. By using Amazon AppStream 2.0, the application can be streamed from AWS to end users. AppStream 2.0 runs the desktop application close to the backend database and application servers within AWS, and streams only the display and input over the network. This significantly reduces the effect of latency between the user and the application backend, improving user experience, especially for latency-sensitive desktop applications, without requiring major changes to the application itself.

Option A uses a PostgreSQL database on EC2, which requires the customer to manage availability, backups, patching, and failover. This does not provide the same managed high-availability guarantees as Aurora or RDS Multi-AZ. It also suggests allocating a full Amazon WorkSpaces desktop per user, which can be more expensive and more complex than streaming only the application via AppStream 2.0.

Option C uses Amazon RDS for PostgreSQL with Multi-AZ configuration, which provides high availability and is a valid managed option. However, it runs the application and presentation layers on Fargate containers behind a Network Load Balancer. NLB is typically used for TCP-level load balancing and is better suited for protocols that require low-level handling, whereas HTTP/HTTPS web applications are commonly placed behind an Application Load Balancer, which provides advanced HTTP routing features. Also, ElastiCache can reduce database load and improve response times but does not directly solve the end-user network latency issue in a desktop client scenario.

Option D is incorrect because Amazon Redshift is a data warehouse service, not an operational PostgreSQL-compatible database for transaction processing. It is not appropriate for hosting the application’s primary relational database. CloudFront accelerates delivery of static and cached web content, not interactive desktop client-server traffic.

Therefore, migrating the database to Aurora PostgreSQL, running the application and presentation layers on EC2 Auto Scaling behind an Application Load Balancer, and using Amazon AppStream 2.0 to deliver the application to remote users (option B) meets the availability requirements, improves user experience, requires relatively little change to the application architecture, and minimizes operational costs compared to more complex or less-managed alternatives.

The company needs the application in the secondary Region to operate independently from the primary Region during disaster recovery. That means both the DynamoDB data and the relational customer data must be present and usable in the secondary Region without cross-Region dependencies at runtime. The company also wants the least development effort, which usually means using native managed replication features rather than building custom replication logic.

For DynamoDB, global tables are the managed multi-Region, multi-active replication feature. Global tables replicate changes between Regions automatically and allow applications in each Region to read and write to local DynamoDB tables, removing dependencies on the primary Region.

For the RDS for MySQL database, the straightforward managed approach for cross-Region disaster recovery with minimal development change is to create a cross-Region read replica in the secondary Region. A read replica uses asynchronous replication to keep data up to date. During a disaster recovery event, the company can promote the read replica in the secondary Region to become a standalone primary database instance. This provides a simple, low-development approach for having the data in the secondary Region. The application in the secondary Region can be configured to use the replica endpoint in normal times and can continue operating with the promoted instance during failover.

Option A combines DynamoDB global tables with an RDS read replica in the secondary Region. This is the least development effort because it uses AWS-managed replication for both data stores and requires only configuration changes and endpoint selection in the secondary application.

Option B is not correct because DAX is a caching layer for DynamoDB, not a replication or disaster recovery mechanism. DAX does not replicate DynamoDB tables into another Region and cannot replace having the DynamoDB data present in the secondary Region.

Option C is incorrect because RDS Multi-AZ is a high availability feature within a single Region, using synchronous replication to a standby in another Availability Zone. Multi-AZ does not create a standby in a different Region, and it does not provide cross-Region DR by itself.

Option D is less desirable because it requires custom development and operations: processing DynamoDB streams, building and operating a replication pipeline, and ensuring correctness and ordering across Regions. This is higher development effort than enabling DynamoDB global tables.

Therefore, using DynamoDB global tables plus an RDS read replica in the secondary Region (option A) meets the DR independence requirement with the least development effort.

Configuring read replicas for Amazon RDS MySQL and using the single reader endpoint in the web application can significantly reduce the load on the backend database tier, improving overall application performance. Additionally, implementing an Amazon ElastiCache cluster between the web application and RDS MySQL instances can further reduce database load by caching frequently accessed data, thereby enhancing the application ' s resilience and scalability. These changes address the root cause of the outage by alleviating the database tier ' s high load and preventing similar issues in the future.

AWS Documentation on Amazon RDS Read Replicas and Amazon ElastiCache provides comprehensive guidance on improving application performance and scalability by offloading read traffic from the primary database and caching common queries. These solutions are in line with AWS best practices for building resilient and scalable web applications.

Comprehensive and Detailed Explanation:

A. Using AWS CloudFormation templates allows for the automation of infrastructure deployment. By parameterizing the templates, resources can be provisioned consistently across multiple Regions, reducing administrative overhead and ensuring infrastructure as code practices.

D. Amazon Route 53 latency-based routing directs user requests to the AWS Region that provides the lowest latency, improving access times for users globally and enhancing the user experience.

E. Enabling DynamoDB Streams and adding a second Region to create a global table ensures that data is replicated across Regions, providing high availability and disaster recovery capabilities.

This combination of steps ensures that the application stack is replicated efficiently across Regions, providing disaster recovery, improved performance, and scalability, all while minimizing manual administrative tasks.

The correct answers are C and D. The IAM user appears to have read-only S3 permissions, but AWS authorization is the result of multiple policy layers. In AWS Organizations, a service control policy can set the maximum permissions available to principals in member accounts. If an OU-level SCP denies or does not allow S3 actions, the IAM user’s read-only policy will not be enough. A permissions boundary can also restrict the maximum permissions an IAM user can exercise, even if an identity-based policy appears to allow access. Bucket policies can affect access to specific buckets and objects, but the symptom says no buckets are listed in the console, which points more strongly to identity-side effective permission limits. ACLs are legacy resource controls and are not the primary place to troubleshoot this issue. (AWS Documentation)

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Migrating the containers that run the application to AWS Fargate for Amazon Elastic Container Service (Amazon ECS) will meet the requirement of not administering the Docker hosting environment. AWS Fargate is a serverless compute engine that runs containerswithout requiring any infrastructure management3. Creating an Amazon Elastic File System (Amazon EFS) file system and adding a volume to the Fargate task definition to point to the EFS file system will meet the requirement of low-latency storage that will automatically scale throughput to meet increased demand. Amazon EFS is a fully managed file system service that provides shared access to data from multiple containers, supports NFSv4 protocol, and offers consistent performance and high availability4. Amazon EFS also supports automatic scaling of throughput based on the amount of data stored in the file system5.

B is correct because it implements a fully serverless ingestion-and-streaming design that decouples request intake from processing and delivers real-time updates to clients over WebSockets. The HTTP API provides a serverless front door for REST requests. Sending requests to Amazon SQS buffers bursts and smooths spikes, preventing the backend from being overwhelmed as traffic grows. The SQS → Lambda pattern provides scalable, asynchronous processing. For the “stream data to clients through WebSockets” requirement, AWS AppSync Events (real-time publish/subscribe) allows clients to subscribe over persistent WebSocket connections and receive pushed updates with minimal latency, while producers (the Lambda function) publish events to channels that subscribers receive.

Why the other options are incorrect:

A: AWS Step Functions is an orchestration service for workflows. It does not provide a native mechanism to “set clients as targets” and push data back to arbitrary REST clients over WebSockets.

C: Amazon EventBridge rules can target AWS services (Lambda, SQS, SNS, etc.), but clients are not valid EventBridge targets for direct WebSocket streaming. This does not solve “stream to clients through WebSockets.”

D: CloudFront Functions are for lightweight request/response manipulation at the edge and cannot act as an origin or run complex business logic. Also, writing to an MQTT topic from a CloudFront Function is not a supported pattern. While AWS IoT Core can support MQTT over WebSockets for clients, the proposed CloudFront Function integration is not valid.

By creating an AMI that has Grafana pre-installed and storing the existing dashboards in Amazon Elastic File System (Amazon EFS) it allows for faster and more efficient scaling, and by creating an Auto Scaling group that uses the new AMI and setting the Auto Scaling group ' s minimum, desired, and maximum number of instances to one and creating an Application Load Balancer that serves at least two Availability Zones, it ensures high availability and minimized downtime.

To attribute AWS costs to specific applications or teams and enable detailed cost analysis and forecasting, the solution architect should recommend the following actions: A. Activating user-defined cost allocation tags for resources associated with each application and team allows for detailed tracking of costs by these identifiers. C. Creating a cost category for each application within AWS Billing and Cost Management enables the organization to group costs according to application, facilitating detailed reporting and analysis. F. Enabling Cost Explorer is essential for analyzing and visualizing AWS spending over time. It provides the capability to view historical costs and forecast future expenses, supporting the company ' s requirement for cost comparison and forecasting.

AWS Billing and Cost Management Documentation: Covers the activation of cost allocation tags, creation of cost categories, and the use of Cost Explorer for cost management.

AWS Tagging Strategies: Provides best practices for implementing tagging strategies that support cost allocation and reporting.

AWS Cost Explorer Documentation: Details how to use Cost Explorer to analyze and forecast AWS costs.

Set up an Amazon Aurora MySQL database. Use AWS Database Migration Service (AWS DMS) to perform continuous data replication from the on-premises database to Aurora. Create an Aurora Replica for the Aurora MySQL database, and move the aggregation jobs to run against the Aurora Replica. Set up collection endpoints as AWS Lambda functions behind an Application Load Balancer (ALB), and use Amazon RDS Proxy to write to the Aurora MySQL database. When the databases are synced, point the collector DNS record to the ALB. Disable the AWS DMS sync task after the cutover from on premises to AWS.

Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability. With RDS Proxy, failover times for Aurora and RDS databases are reduced by up to 66%

Option D best addresses all aspects of the problem:

Provisioned Concurrency for Lambda ensures that the Lambda functions have pre-initialized execution environments ready, eliminating cold start delays and ensuring low-latency performance during traffic spikes.

RDS Reserved Instances provide significant cost savings for workloads with consistent and predictable usage, which aligns with the company’s usage patterns.

AWS WAF integrated with CloudFront directly addresses the security concern by providing application layer protection against SQL injection and web exploit attempts.

This solution ensures scalability, performance consistency, cost savings, and security protection without introducing unnecessary complexity.

To offer services to other companies using AWS without traversing the internet, creating a VPC Endpoint Service hosted behind an Application Load Balancer (ALB) and making it available over AWS Direct Connect (DX) is the most suitable solution. This approach ensures that the service traffic remains within the AWS network, adhering to the requirement that connectivity must not traverse the internet. An ALB is capable of handlingHTTP/HTTPS traffic, making it appropriate for web-based services. Utilizing DX for connectivity between the on-premises data center and AWS further secures and optimizes the network path.

AWS Direct Connect Documentation: Explains how to set up DX for private connectivity between AWS and an on-premises network.

Amazon VPC Endpoint Services (AWS PrivateLink) Documentation: Provides details on creating and configuring endpoint services for private, secure access to services hosted in AWS.

AWS Application Load Balancer Documentation: Offers guidance on configuring ALBs to distribute HTTP/HTTPS traffic efficiently.

Enabling all features for the organization will enable using AWS Firewall Manager to centrally configure and manage firewall rules across multiple AWS accounts1. Using AWS Firewall Manager to deploy AWS WAF rules in all accounts for all CloudFront distributions will enable providing baseline protection for the OWASP top 10 web application vulnerabilities2. AWS Firewall Manager supports AWS WAF rules that can help protect against common web exploits such as SQL injection and cross-site scripting3. Configuring redirection of HTTP requests to HTTPS requests in CloudFront will enable encrypting the data in transit using SSL/TLS.

The company should create a Network Load Balancer (NLB) and associate it with one static IP address in multiple Availability Zones. The company should also create an ALB-type target group for the NLB and add the existing ALB. The company should add the NLB IP addresses to the firewall appliance and update the clients to connect to the NLB. This solution will allow traffic flow to AWS from the on-premises network by using static IP addresses that can be added to the firewall appliance’s allow list. The NLB will forward requests to the ALB, which will use path-based routing to forward requests to the target groups.

" A usage plan specifies who can access one or more deployed API stages and methods—and also how much and how fast they can access them. The plan uses API keys to identify API clients and meters access to the associated API stages for each key. It also lets you configure throttling limits and quota limits that are enforced on individual client API keys. " https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html

A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action on IPs with rates that go over a limit. You set the limit as the number of requests per 5-minute time span...... The following caveats apply to AWS WAF rate-based rules: The minimum rate that you can set is 100. AWS WAF checks the rate of requests every 30 seconds, and counts requests for the prior five minutes each time. Because of this, it ' s possible for an IP address to send requests at too high a rate for 30 seconds before AWS WAF detects and blocks it. AWS WAF can block up to 10,000 IP addresses. If more than 10,000 IP addresses send high rates of requests at the same time, AWS WAF will only block 10,000 of them. " https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-type-rate-based.html

The company has many S3 buckets and many identities across multiple AWS accounts. It currently uses S3 bucket policies for granular authorization. As requirements grow, the bucket policies have become very large and have hit the maximum size limits. The company needs a way to express granular, identity-based access to S3 resources without relying on increasingly complex bucket policies.

AWS provides S3 Access Grants to centrally manage S3 data access based on identities. S3 Access Grants integrate with IAM Identity Center and external identity providers such as Microsoft Entra ID (formerly Azure AD). With S3 Access Grants, you define grants that associate specific identities or groups (from IAM Identity Center) with permissions on specific S3 buckets, prefixes, or objects. Access Grants maintain a mapping between identities and S3 resources without requiring large, per-bucket policies.

Option A describes exactly this approach. You create an S3 Access Grant configuration associated with the IAM Identity Center instance that is already integrated with the company’s IdP. You then define S3 Access Grants for the various user groups according to business requirements, specifying the appropriate S3 buckets and prefixes. When users access S3 through AWS tools that support S3 Access Grants, temporary credentials are issued that reflect the grants. This centralizes authorization management, reduces the size and complexity of S3 bucket policies, and minimizes operational overhead because you manage permissions per identity group rather than constantly editing long bucket policies.

Option B uses S3 access points and Object Lambda Access Points with a Lambda function for data filtering. This is more suitable for content transformation or redaction rather than primary authorization management. It adds operational complexity by requiring custom Lambda code and Object Lambda configuration, and it does not directly solve the problem of large bucket policies for fine-grained permissions.

Option C uses per-bucket S3 access points with attached policies. While access points can simplify some aspects of access control and allow per-application policies, using many access points with detailed policies can still lead to significant policy complexity. It also does not take advantage of the existing IAM Identity Center integration and does not centrally align permissions with identity groups.

Option D uses AWS Organizations service control policies (SCPs). SCPs are intended to define high-level guardrails and maximum permissions at the account or organizational unit level, not detailed, per-bucket or per-prefix access control for individual users or groups. Using SCPs to express granular S3 bucket permissions would be complex, hard to maintain, and not aligned with the purpose of SCPs.

Therefore, using S3 Access Grants integrated with IAM Identity Center (option A) provides a centralized, identity-aware, low-overhead way to manage granular S3 access without hitting S3 bucket policy size limits.

Creating the Template:

Start by creating a CloudFormation template that includes all the VPC resources. This template should accurately reflect the current state and configuration of the VPC.

Using the CloudFormation Console:

Open the AWS Management Console and navigate to CloudFormation.

Choose " Create stack " and then select " With existing resources (import resources) " .

Specifying the Template:

Upload the previously created template or specify the Amazon S3 URL where the template is stored.

Identifying the Resources:

On the " Identify resources " page, provide the identifiers for each VPC resource you wish to import. For example, for anAWS::EC2::VPCresource, use the VPC ID as the identifier.

Creating the Stack:

Complete the stack creation process by providing stack details and reviewing the changes. This will create a change set that includes the import operation.

Executing the Change Set:

Execute the change set to import the resources into the CloudFormation stack, making them managed by CloudFormation.

Verification and Drift Detection:

After the import is complete, run drift detection to ensure the actual configuration matches the template configuration.

This approach allows the company to manage their VPC and other resources via CloudFormation without the need to recreate resources, ensuring a smooth transition to automated infrastructure management.

References

Creating a stack from existing resources - AWS CloudFormation​(AWS Documentation)​.

Generating templates for existing resources - AWS CloudFormation​(AWS Documentation)​.

Bringing existing resources into CloudFormation management​(AWS Documentation)​.

Option C is the correct and most efficient solution, aligning with AWS best practices for secure and private connectivity:

Create VPC Endpoints for Systems Manager and Amazon S3:

Systems Manager VPC Endpoints: By creating interface VPC endpoints for Systems Manager (com.amazonaws.region.ssm, com.amazonaws.region.ec2messages, and com.amazonaws.region.ssmmessages), the EC2 instances can communicate with Systems Manager services without requiring internet access. This setup ensures that patching operations can be conducted securely within the AWS network.

Amazon S3 VPC Endpoint: A gateway VPC endpoint for Amazon S3 (com.amazonaws.region.s3) allows EC2 instances to access S3 buckets privately. This is essential for accessing application data stored in S3 without traversing the public internet.

D is correct because thehealth check grace perioddefines how long Auto Scaling waits after launching an instance before checking its health status. With the larger content added to the S3 bucket, the instance initialization (via user data) is taking longer. Increasing the grace period allows the instance time to complete startup tasks before it’s marked as unhealthy.

Option A would not necessarily reduce startup time — the issue is likely network latency or the size of the content.

Option B only affects the timeout for health check responses, not the delay before they begin.

Option C is not applicable unless the health check path itself is invalid (which isn ' t mentioned).

C is correct because the Storage Gateway Volume Gateway in cache mode supportsiSCSI block storage, which is exactly what legacy applications need. It also caches frequently accessed data locally for low-latency performance and integrates with AWS Backup for point-in-time copies.

A is incorrect — tape gateway is designed for archival backup only.

B and D are incorrect because FSx and file gateway offerfile(NFS/SMB) storage, not block/iSCSI support.

AWS Application Migration Service:

AWS Application Migration Service (MGN) facilitates the migration of virtual machines (VMs) to AWS without installing additional software on the VMs. This agentless service helps in seamlessly migrating workloads to AWS.

AWS Migration Hub Strategy Recommendations:

AWS Migration Hub Strategy Recommendations offer insights and guidance for planning and implementing migration strategies. It helps in generating a Total Cost of Ownership (TCO) report by automatically importing discovery data from the VMs.

Generating the TCO Report:

The combined use of AWS Application Migration Service and Migration Hub Strategy Recommendations enables the automatic import of discovery data and the generation of an accurate TCO report, ensuring a smooth and cost-effective migration process.

References

AWS Migration Hub Strategy Recommendations​(AWS Documentation)​.

C is correct: AWS Transfer Family can store uploaded files directly into Amazon S3.S3 event notificationscan triggerAWS Glue jobsto transform the data. Upon successful transformation, Glue can send a message toAmazon SQS, enabling event-driven architecture with minimal management.

A and D involve cron jobs or scheduled Glue jobs, which increase operational overhead and delay.

B (EMR) is overkill for frequent, lightweight transformations.

https://docs.aws.amazon.com/AmazonECR/latest/userguide/ecr-eventbridge.html " Activating an AWS Step Functions state machine " https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-creating-lambda-state-machine.html

B is correct becauseAWS/Usagenamespace in CloudWatch provides built-in metrics for service quota usage. You can create an alarm forFargate task count usageand notify the dev team usingSNSwhen it reaches 80% of the quota.

A misuses “Sample Count” and doesn’t relate to service quota.

C is overly complex and not needed — native metrics exist.

D misuses Config (meant for compliance, not utilization).

This option allows the company to use a single VPC to host multiple test environments that are isolated from each other by using different subnets and security groups1. By including a transit gateway attachment and related routing configurations, the company can enable the test environmentsto communicate with the central server in the on-premises data center through a VPN connection2. By using AWS CloudFormation to deploy all test environments into the VPC, the company can automate the creation and deletion of test environments without any manual intervention3. This option also minimizes the operational overhead by reducing the number of VPCs, accounts, and resources that need to be managed.

Working with VPCs and subnets

Working with transit gateways

Working with AWS CloudFormation stacks

The company should create an Amazon CloudFront distribution with the ALB as the origin. The company should add a custom header and random value on the CloudFront domain. The company should configure the ALB to conditionally forward traffic if the header and value match. The company should also deploy an AWS WAF web ACL that includes an appropriate rule group. The company should associate the web ACL with the Amazon CloudFront distribution. This solution will meet the requirements most cost-effectively because Amazon CloudFront is a fast content delivery network (CDN) service that securely deliversdata, videos, applications, and APIs to customers globally with lowlatency, hightransfer speeds, all within a developer-friendly environment1. By creating an Amazon CloudFront distribution with the ALB as the origin, the company can improve the performance andavailability of its application by caching static content at edge locations closer to end users. By adding a custom header and random value on the CloudFront domain, the company can prevent direct access to the ALB and ensure that only requests from CloudFront are forwarded to the ECS tasks. By configuring the ALB to conditionally forward traffic if the header and value match, the company can implement origin access identity (OAI) for its ALB origin. OAI is a feature that enables you to restrict access to your content by requiring users to access your content through CloudFront URLs2. By deploying an AWS WAF web ACL that includes an appropriate rule group, the company can prevent attacks and ensure business continuity with minimal service interruptions during an ongoing attack. AWS WAF is a web application firewall that lets you monitor and control web requests that are forwarded to your web applications. You can use AWS WAF to define customizable web security rules that control which traffic can access your web applications and which traffic should be blocked3. By associating the web ACL with the Amazon CloudFront distribution, the company can apply the web security rules to all requests that are forwarded by CloudFront.

The other options are not correct because:

Deploying the application in two AWS Regions and configuring Amazon Route 53 to route to both Regions with equal weight would not prevent attacks or ensure business continuity. Amazon Route 53 is a highly available and scalable cloud Domain Name System (DNS) web service that routes end users to Internet applications by translating names like www.example.com into numeric IP addresses4. However, routing traffic to multiple Regions would not protect against attacks or provide failover in case of an outage. It would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Configuring auto scaling for Amazon ECS tasks and creating a DynamoDB Accelerator (DAX) cluster would not prevent attacks or ensure business continuity. Auto scaling is a feature that enables you to automatically adjust your ECS tasks based on demand or a schedule. DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement. However, these features would not protect against attacks or provide failover in case of an outage. They would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Configuring Amazon ElastiCache to reduce overhead on DynamoDB would not prevent attacks or ensure business continuity. Amazon ElastiCache is a fully managed in-memory data store service that makes it easy to deploy, operate, and scale popular open-source compatible in-memory data stores. However, this service would not protect against attacks or provide failover in case of an outage. It would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

The company should use AWS Amplify to create a static website for uploads of media files. The company should use Amplify Hosting to serve the website through Amazon CloudFront. The company should use Amazon S3 to store the uploaded media files. The company should use Amazon Cognito to authenticate users. This solution will meet the requirements with the least operational overhead because AWS Amplify is a complete solution that lets frontend web and mobile developers easily build, ship, and host full-stack applications on AWS, with the flexibility to leverage the breadth of AWS services as use cases evolve. No cloud expertise needed1. By using AWS Amplify, the company can refactor the application to a serverless architecture that reduces operational complexity and costs. AWS Amplify offers the following features and benefits:

Amplify Studio: A visual interface that enables you to build and deploy a full-stack app quickly, including frontend UI and backend.

Amplify CLI: A local toolchain that enables you to configure and manage an app backend with just a few commands.

Amplify Libraries: Open-source client libraries that enable you to build cloud-powered mobile and web apps.

Amplify UI Components: Open-source design system with cloud-connected components for building feature-rich apps fast.

Amplify Hosting: Fully managed CI/CD and hosting for fast, secure, and reliable static and server-side rendered apps.

By using AWS Amplify to create a static website for uploads of media files, the company can leverage Amplify Studio to visually build a pixel-perfect UI and connect it to a cloud backend in clicks. By using Amplify Hosting to serve the website through Amazon CloudFront, the company can easily deploy its web app or website to the fast, secure, and reliable AWS content delivery network (CDN), with hundreds of points of presence globally. Byusing Amazon S3 to store the uploaded media files, the company can benefit from a highly scalable, durable, and cost-effective object storage service that can handle any amount of data2. By using Amazon Cognito to authenticate users, the company can add user sign-up, sign-in, and access control to its web app with a fully managed service that scales to support millions of users3.

The other options are not correct because:

Using AWS Application Migration Service to migrate the application server to Amazon EC2 instances would not refactor the application or accelerate development. AWS Application Migration Service (AWS MGN) is a service that enables you to migrate physical servers, virtual machines (VMs), or cloud servers from any source infrastructure to AWS without requiring agents or specialized tools. However, this would not address the challenges of overutilization and data uploads failures. It would also not reduce operational overhead or costs compared to a serverless architecture.

Creating a static website for uploads of media files and using AWS AppSync to create an API would not be as simple or fast as using AWS Amplify. AWS AppSync is a service that enables you to create flexible APIs for securely accessing, manipulating, and combining data from one or more data sources. However, this would require more configuration and management than using Amplify Studio and Amplify Hosting. It would also not provide authentication features like Amazon Cognito.

Setting up AWS IAM Identity Center (AWS Single Sign-On) to give users the ability to sign in to the application would not be as suitable as using Amazon Cognito. AWS Single Sign-On (AWS SSO) is a service that enables you to centrally manage SSO access and user permissions across multiple AWS accounts and business applications. However, this service is designed for enterprise customers who need to manage access for employees or partners across multiple resources. It is not intended for authenticating end users of web or mobile apps.

https://aws.amazon.com/premiumsupport/knowledge-center/aws-batch-requests-error/

https://aws.amazon.com/premiumsupport/knowledge-center/api-gateway-429-limit/

The main problem is that one client is generating a large number of PUT requests, resulting in increased errors. Because the clients are authenticated by API keys and the problematic traffic is concentrated among a small set of clients, API Gateway usage plans and throttling are a direct control mechanism to limit the request rate per client (or per API key). The API is noncritical and clients can tolerate retries, so returning throttling responses is acceptable as long as clients handle them correctly. The goal is to reduce visible customer-facing errors and protect the API from being overwhelmed by a single client.

API Gateway usage plans can set request throttling limits (rate and burst) per API key. When a client exceeds the configured throttle, API Gateway returns HTTP 429 (Too Many Requests). If the client application is designed to treat 429 as a normal, retryable condition rather than a fatal error, the user experience improves because the client can back off and retry instead of surfacing raw failures. This approach prevents one noisy client from degrading service for other users and reduces the overall error rate.

Option B addresses the root cause (excess requests from a particular client) by enforcing throttling at the API Gateway layer and improving client behavior when throttled.

Option A focuses on client-side retries and error handling but does not enforce fairness or prevent a single client from consuming disproportionate API capacity. Without throttling, the same client can continue to overload downstream Lambda and DynamoDB capacity and still cause errors for everyone.

Option C (API caching) does not help for PUT requests because PUT operations are writes and are generally not cacheable in a way that reduces write load. Caching is mainly beneficial for GET responses and read-heavy workloads.

Option D (reserved concurrency) can protect Lambda capacity for critical functions, but it can also cause throttling at the Lambda layer and does not directly address controlling the request rate per API key. It also does not fix the issue at the API front door where the noisy client should be managed.

Therefore, implementing API Gateway throttling via a usage plan and ensuring clients handle 429 responses properly is the best recommendation.

Using a DHCP options set in AWS allows EC2 instances to automatically receive network configuration information, including the IP address of the on-premises NTP server.

This ensures that all EC2 instances in the VPC consistently use the on-premises atomic clock as their authoritative time source, maintaining the same time synchronization as the existing environment.

By allowing NTP traffic over the Direct Connect connection and ensuring proper security group and network ACL configurations, this approach eliminates the need to manually configure each EC2 instance’s NTP settings, significantly reducing administrative overhead and ensuring consistent, accurate time across the environment.

This aligns with AWS best practices for extending on-premises time synchronization to EC2 instances in a hybrid environment.

Lambda has a maximum timeout of 900 seconds, or 15 minutes. Since the function is already configured with the maximum timeout and still fails, the workload should move to a containerized compute model that can run longer. The least operational overhead option is Amazon ECS with AWS Fargate. The application code should be packaged as a Docker container image and stored in Amazon ECR, which ECS task definitions can reference. EventBridge can invoke ECS tasks directly and supports ECS parameters, including the Fargate launch type. This avoids keeping Lambda in the execution path and removes the need to manage EC2 container instances. Storing container images in S3 is not the standard ECS deployment model, and Step Functions does not directly “use” a Docker image as described. (AWS Documentation)

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https://aws.amazon.com/premiumsupport/knowledge-center/dynamodb-global-table-stream-lambda/?nc1=h_ls

https://aws.amazon.com/blogs/aws/new-use-aws-cloudformation-stacksets-for-multiple-accounts-in-an-aws-organization/

AWS Organizations allows the management of multiple AWS accounts as a single entity and AWS CloudFormation StackSets allows creating, updating, and deleting stacks across multiple accounts and regions in an organization. This solution allows creating a single CloudFormation template that can be deployed across multiple accounts and regions, and also allows for the management of access and permissions for the different accounts through the use of IAM roles and policies in the management account.

B. Add an outbound rule to the EC2 instances ' security group. Specify the DB cluster ' s security group as the destination over the default Aurora port. This allows theinstances to make outbound connections to the DB cluster on the default Aurora port. C. Add an inbound rule to the DB cluster ' s security group. Specify the EC2 instances ' security group as the source over the default Aurora port. This allows connections to the DB cluster from the EC2 instances on the default Aurora port.

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-overview-developer-experience.html

This option will complete the migration to AWS in the least amount of time because it uses two AWS services that are designed to simplify and accelerate data transfers and migrations. AWS Application Migration Service (AWS MGN) is a highly automated lift-and-shift solution that helps you migrate applications from any source infrastructure that runs supported operating systems to AWS1. It replicates your source servers into your AWS account and automatically converts and launches them on AWS so you can quickly benefit from the cloud1. You can use AWS MGN to migrate your on-premises VMware VMs to AWS by configuring a connection to your VMware cluster and creating a replication job for the VMs2. This process will minimize the time-intensive, error-prone manual processes of exporting and importing VM images.

AWS DataSync is an online data movement and discovery service that simplifies and accelerates data migrations to AWS and helps you move data quicklyand securelybetween on-premises storage, edge locations, other cloud providers, and AWS Storage3. It can transfer data between Network File System (NFS) shares, Server Message Block (SMB) shares,Hadoop Distributed File Systems (HDFS), self-managed object storage, AWS Snowcone, Amazon Simple Storage Service (Amazon S3) buckets, Amazon Elastic File System (Amazon EFS) file systems, Amazon FSx for Windows File Server file systems, Amazon FSx for Lustre file systems, Amazon FSx for OpenZFS file systems, and Amazon FSx for NetApp ONTAP file systems3. You can use AWS DataSync to copy your on-premises NFS server data to an Amazon EFS file system over the Direct Connect connection4. This process will leverage the high bandwidth and low latency of Direct Connect and the encryption and data integrity validation of DataSync.

A is the correct answer because it uses Amazon DocumentDB (with MongoDB compatibility) as a key-value database that can scale based on demand and supports encryption in transit and at rest. Amazon DocumentDB is a fully managed document database service that is designed to be compatible with the MongoDB API. It is a NoSQL database that is optimized for storing, indexing, and querying JSON data. Amazon DocumentDB supports encryption in transit using TLS and encryption at rest using AWS Key Management Service (AWS KMS). Amazon DocumentDB also supports provisioned IOPS volumes that can scale up to 64 TiB of storage and 256,000 IOPS per cluster. To connect to Amazon DocumentDB, you can use the instance endpoint, which connects to a specific instance in the cluster, or the cluster endpoint, which connects to the primary instance or one of the replicas in the cluster. Using the cluster endpoint is recommended for high availability and load balancing purposes. References:

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

https://docs.aws.amazon.com/documentdb/latest/developerguide/security.encryption.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/limits.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/connecting.html

Enable AWS Security Hub:

Navigate to the AWS Security Hub console in your management account and enable Security Hub. This process integrates Security Hub with AWS Control Tower, allowing you to manage and monitor security findings across all accounts within your organization.

Designate a Delegated Administrator:

In AWS Organizations, designate one of the AWS accounts as the delegated administrator for Security Hub. This account will have the responsibility to manage and oversee the security posture of all accounts within the organization.

Deploy Controls Across Accounts:

Use AWS Security Hub to automatically enable security controls across all AWS accounts in the organization. This provides a centralized view of the security state of all accounts and ensures continuous monitoring and compliance.

Utilize AWS Security Hub Features:

Leverage the capabilities of Security Hub to aggregate security alerts, run continuous security checks, and generate findings based on the AWS Foundational Security Best Practices. Security Hub integrates with other AWS services like AWS Config, Amazon GuardDuty, and AWS IAM Access Analyzer to enhance security monitoring and remediation.

By integrating AWS Security Hub with AWS Control Tower and using a delegated administrator account, you can achieve a centralized and comprehensive view of your organization’s security posture, facilitating effective management and remediation of security issues.

References

AWS Security Hub now integrates with AWS Control Tower【77】

AWS Control Tower and Security Hub Integration【76】

AWS Security Hub Features【79】

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/migrate-files-to-fsx-datasync.html

Deploying the shared libraries and custom classes to a Docker image and uploading the image to Amazon Elastic Container Registry (Amazon ECR) and creating a Lambda layer that uses the Docker image as the source. Then, deploying the API ' s Lambda functions as Zip packages and configuring the packages to use the Lambda layer would meet the requirements for simplifying the deployment and optimizing for code reuse.

A Lambda layer is a distribution mechanism for libraries, custom runtimes, and other function dependencies. It allows you to manage your in-development function code separately from your dependencies, this way you can easily update your dependencies without having to update your entire function code.

By deploying the shared libraries and custom classes to a Docker image and uploading the image to Amazon Elastic Container Registry (ECR), it makes it easy to manage and version the dependencies. This way, the company can use the same version of the dependencies across different Lambda functions.

By creating a Lambda layer that uses the Docker image as the source, the company can configure the API ' s Lambda functions to use the layer, reducing the need to include the dependencies in each function package, and making it easy to update the dependencies across all functions at once.

https://docs.aws.amazon.com/compute-optimizer/latest/APIReference/API_ExportLambdaFunctionRecommendations.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-examples.html

The company should use AWS IoT Greengrass to deploy the ML model to the local server and provide local feedback to the factory workers. AWS IoT Greengrass is a service that extends AWS cloud capabilities to local devices, allowing them to collect and analyze data closer to the source of information,react autonomously to local events, and communicate securely with each other on local networks1. AWS IoT Greengrass also supports MLinference at the edge, enabling devices to run ML models locally without requiring internet connectivity2.

The other options are not correct because:

Setting up an Amazon Kinesis video stream from each IP camera to AWS would not work if the factory’s internet connectivity is down. It would also incur unnecessary costs and latency to stream video data to the cloud and back.

Ordering an AWS Snowball device would not be a scalable or cost-effective solution for deploying the ML model. AWS Snowball is a service that provides physical devices for data transfer and edge computing, but it is not designed for continuous operation or frequent updates3.

Deploying Amazon Monitron devices on each IP camera would not work because Amazon Monitron is a service that monitors the condition and performance of industrial equipment using sensors and machine learning, not cameras4.

Cross account role should be created in destination(member) account. The role has trust entity to master account.