Data-Engineer-Associate AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library for data analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files. References:

AWS Glue

Working with Python Shell Jobs

pandas

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.

AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.

Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.

Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.

Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.

Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.

Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References:

What is Amazon Athena?

Data Catalog and crawlers in AWS Glue

AWS Glue Data Catalog

Columnar Storage Formats

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

AWS Glue Schema Registry

What is AWS Glue?

Amazon Redshift Serverless

Amazon Redshift provisioned clusters

[Querying external data using Amazon Redshift Spectrum]

[Using stored procedures in Amazon Redshift]

[What is AWS Lambda?]

[PartiQL for Amazon Athena]

[Federated queries in Amazon Athena]

[Amazon Athena pricing]

[Top 10 performance tuning tips for Amazon Athena]

[AWS Glue ETL jobs]

[AWS Lake Formation jobs]

According to the guide:

“For integrating streaming data from Amazon MSK into Amazon Redshift efficiently and cost-effectively, AWS Glue streaming jobs can process and transform the data, storing it in Amazon S3. Amazon Redshift Spectrum can then directly query the data from S3, minimizing operational overhead and reducing storage costs.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This setup offers:

Low latency via Glue streaming.

Low storage cost by using Parquet/ORC on S3.

Minimal operational overhead by avoiding complex pipelines or constantly updated materialized views.

The Redshift UNLOAD command is specifically designed to export query results to Amazon S3, and AWS Glue can orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:

“Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.

Problem Analysis:

The company needs to migrate both an application and an on-premises Apache Kafka server to AWS.

Incremental updates from an on-premises Oracle database are processed by Kafka.

The solution must follow a replatform migration strategy, prioritizing minimal changes and low management overhead.

Key Considerations:

Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.

The solution must provide a managed Kafka service to minimize operational burden.

Low overhead solutions like serverless services are preferred.

Solution Analysis:

Option A: Kinesis Data Streams

Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.

This option would require significant application refactoring, which does not align with the replatform strategy.

Option B: MSK Provisioned Cluster

Managed Kafka service with fully configurable clusters.

Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.

Option C: Amazon Kinesis Data Firehose

Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.

Not suitable for Kafka-based applications.

Option D: MSK Serverless

MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.

Automatically scales based on workload, reducing operational overhead.

Ideal for replatform migrations, as it requires minimal changes to the application.

Final Recommendation:

Amazon MSK Serverless is the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.

Amazon MSK Serverless Overview

Comparison of Amazon MSK and Kinesis

Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.

A data mesh approach in AWS typically uses Lake Formation for domain-level access control and Athena for cross-domain querying through federated views. CloudTrail ensures auditing.

“For data mesh architectures, use AWS Lake Formation for fine-grained access control and Athena views for cross-domain analysis. Enable CloudTrail to audit access.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This ensures scalability, security, and compliance across domains.

Problem Analysis:

The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.

Data updates must propagate quickly to OpenSearch for analytics or search purposes.

Key Considerations:

DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).

Integration with AWS Lambda allows seamless processing of these changes.

OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.

Solution Analysis:

Option A: Step Functions with Periodic Export

Not suitable for near-real-time updates; introduces significant latency.

Operationally complex to manage periodic exports and S3 data ingestion.

Option B: AWS Glue Job

AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.

Option C: DynamoDB Streams + Lambda

DynamoDB Streams capture changes in near real time.

Lambda can process these streams and use the OpenSearch API to update the index.

This approach provides low latency and seamless integration with minimal operational overhead.

Option D: Custom OpenSearch Plugin

Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.

Implementation Steps:

Enable DynamoDB Streams for the relevant DynamoDB tables.

Create a Lambda function to process stream records:

Parse insert, update, and delete events.

Use OpenSearch APIs to index or update documents based on the event type.

Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.

Monitor and log errors for debugging and operational health.

Amazon DynamoDB Streams Documentation

AWS Lambda and DynamoDB Integration

Amazon OpenSearch Service APIs

For near real-time processing of new files in S3, event-driven ingestion is optimal. S3 Event Notifications can trigger AWS Lambda to immediately load data into Amazon Redshift, eliminating latency associated with batch scheduling.

“Event-based triggers using S3 notifications and Lambda functions are effective for near real-time ingestion pipelines into Amazon Redshift.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C (Glue bookmarks) is best for batch jobs, and zero-ETL applies to Aurora to Redshift, not S3.

Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.

Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.

“For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

S3 Lifecycle policies automate data retention and deletion. By specifying an expiration rule for 7 years, objects older than that period are permanently deleted without manual intervention.

“To automatically delete aged data, configure an S3 Lifecycle rule with an expiration policy for objects older than the retention period.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option A (AWS DMS): Lets you replicate only selected tables from Aurora to Redshift. It supports ongoing replication (CDC) and reduces unnecessary data transfers.

Option C (Zero-ETL with Aurora to Redshift): This new integration allows real-time, serverless, and low-maintenance data replication. It's designed to reduce operational overhead drastically.

Other options introduce manual processing or inefficient query access (Option D). Glue workflows (Option E) add unnecessary complexity and lag for near-real-time needs.

“Zero-ETL integration between Amazon Aurora and Redshift enables real-time analytics without building complex pipelines.”

Step 1: Understanding the Problem

The company collects clickstream data via Amazon Kinesis Data Streams and stores it in JSON format in Amazon S3 using Kinesis Data Firehose. They use Amazon Athena to query the data, but they want to reduce Athena costs while maintaining the same data pipeline.

Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format like Apache Parquet) is a key solution to lowering costs.

Step 2: Why Option A is Correct

Option A provides a straightforward way to reduce costs with minimal management overhead:

Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.

Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.

AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one-time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.

ALTER TABLE ADD PARTITION: This command updates Athena's table metadata to reflect the new partitions, ensuring that future queries target only the required data.

Step 3: Why Other Options Are Not Ideal

Option B (Apache Spark on EMR) introduces higher management effort by requiring the setup of Apache Spark jobs and an Amazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.

Option C (Kinesis and Apache Flink) is a more complex solution involving Apache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.

Option D (AWS Lambda with Firehose) suggests using AWS Lambda to convert records in real time. While Lambda can work in some cases, it's generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations. Additionally, running parallel Glue jobs further complicates the setup.

Step 4: How Option A Minimizes Costs

By using Apache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.

Firehose natively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.

The AWS Glue ETL job for historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.

Conclusion:

Option A meets the requirement to reduce Athena costs without recreating the data pipeline, using Firehose’s native support for Apache Parquet and a simple one-time AWS Glue ETL job for existing data. This approach involves minimal management effort compared to the other solutions.

Redshift data sharing is a feature that enables you to share live data across different Redshift clusters without the need to copy or move data. Data sharing provides secure and governed access to data, while preserving the performance and concurrency benefits of Redshift. By setting up the sales team BI cluster as a consumer of the ETL cluster, the company can share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution also minimizes the usage of the computing resources of the ETL cluster, as the data sharing does not consume any storage space or compute resources from the producer cluster. The other options are either not feasible or not efficient. Creating materialized views or database views would require the sales team to have direct access to the ETL cluster, which could interfere with the critical analysis tasks. Unloading a copy of the data from the ETL cluster to an Amazon S3 bucket every week would introduce additional latency and cost, as well as create data inconsistency issues. References:

Sharing data across Amazon Redshift clusters

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

The DatasetMatch rule in DQDL checks for full value equivalence between mapped fields. A value of 1.0 indicates a 100% match. The correct syntax and metric for an exact match scenario are:

“Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Options with “100” use incorrect syntax since DQDL uses floating-point scores (e.g., 1.0, 0.95), not percentages.

 QuickSight does not have access to the S3 bucket and QuickSight does not have access to decrypt S3 data are two possible factors that could cause the permissions-related errors. Amazon QuickSight is a business intelligence service that allows you to create and share interactive dashboards based on various data sources, including Amazon Athena. Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. To use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket, you need to grant QuickSight access to both Athena and S3, as well as any encryption keys that are used to encrypt the S3 data. If QuickSight does not have access to the S3 bucket or the encryption keys, it will not be able to read the data from Athena and display it on the dashboard, resulting in an error message that indicates insufficient permissions.

The other options are not factors that could cause the permissions-related errors. Option A, there is no connection between QuickSight and Athena, is not a factor, as QuickSight supports Athena as a native data source, and you can easily create a connection between them using the QuickSight console or the API. Option B, the Athena tables are not cataloged, is not a factor, as QuickSight can automatically discover the Athena tables that are cataloged in the AWS Glue Data Catalog, and you can also manually specify the Athena tables that are not cataloged. Option E, there is no IAM role assigned to QuickSight, is not a factor, as QuickSight requires an IAM role to access any AWS data sources, including Athena and S3, and you can create and assign an IAM role to QuickSight using the QuickSight console or the API. References:

Using Amazon Athena as a Data Source

Granting Amazon QuickSight Access to AWS Resources

Encrypting Data at Rest in Amazon S3

The data engineer needs to detect custom categories of PII within the data lake using AWS Glue DataBrew. While DataBrew provides standard data quality rules, the solution must support custom PII categories.

Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.This option is the most efficient because DataBrew allows the creation of custom data quality rules that can be applied to detect specific data patterns, including custom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.

Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.

Lake Formation requires that S3 locations be registered before they can be accessed by AWS Glue crawlers, jobs, or queries. Registering the location associates it with the Lake Formation permissions framework.

“Before AWS Glue crawlers can access an S3 path managed by Lake Formation, the S3 location must be registered in Lake Formation.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handles idempotency. By embedding a unique ID in each record, the application can identify and remove duplicate records during processing.

Exactly-Once Processing:

Kinesis Data Streams does not natively support exactly-once processing. Therefore, idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.

This pattern is widely used for achieving exactly-once semantics in distributed systems.

AWS Step Functions natively emits state change events to Amazon EventBridge, which can trigger an Amazon SNS notification. This is the most direct and real-time way to alert on success/failure without relying on custom logging or polling.

“Step Functions automatically emits status changes that EventBridge can capture to trigger alerts or workflows. Use EventBridge to invoke an SNS topic for real-time alerts on job status.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This provides real-time alerting and the least operational overhead.

To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.

A. AWS Glue DataBrew for Masking:

AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.

 Option C is the best solution to meet the requirements with the least effort because server-side encryption with AWS KMS keys (SSE-KMS) is a feature that allows you to encrypt data at rest in Amazon S3 using keys managed by AWS Key Management Service (AWS KMS). AWS KMS is a fully managed service that enables you to create and manage encryption keys for your AWS services and applications. AWS KMS also allows you to define granular access policies for your keys, such as who can use them to encrypt and decrypt data, and under what conditions. By using SSE-KMS, you can protect your S3 objects by using encryption keys that only specific employees can access, without having to manage the encryption and decryption process yourself.

Option A is not a good solution because it involves using AWS CloudHSM, which is a service that provides hardware security modules (HSMs) in the AWS Cloud. AWS CloudHSM allows you to generate and use your own encryption keys on dedicated hardware that is compliant with various standards and regulations. However, AWS CloudHSM is not a fully managed service and requires more effort to set up and maintain than AWS KMS. Moreover, AWS CloudHSM does not integrate with Amazon S3, so you have to configure the process that writes to S3 to make calls to CloudHSM to encrypt and decrypt the objects, which adds complexity and latency to the data protection process.

Option B is not a good solution because it involves using server-side encryption with customer-provided keys (SSE-C), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that you provide and manage yourself. SSE-C requires you to send your encryption key along with each request to upload or retrieve an object. However, SSE-C does not provide any mechanism to restrict access to the keys that encrypt the objects, so you have to implement your own key management and access control system, which adds more effort and risk to the data protection process.

Option D is not a good solution because it involves using server-side encryption with Amazon S3 managed keys (SSE-S3), which is a feature that allows you to encrypt data at rest in Amazon S3 using keys that are managed by Amazon S3. SSE-S3 automatically encrypts and decrypts your objects as they are uploaded and downloaded from S3. However, SSE-S3 does not allow you to control who can access the encryption keys or under what conditions. SSE-S3 uses a single encryption key for each S3 bucket, which is shared by all users who have access to the bucket. This means that you cannot restrict access to the keys that encrypt the objects by specific employees, which does not meet the requirements.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Protecting Data Using Server-Side Encryption with AWS KMS–Managed Encryption Keys (SSE-KMS) - Amazon Simple Storage Service

What is AWS Key Management Service? - AWS Key Management Service

What is AWS CloudHSM? - AWS CloudHSM

Protecting Data Using Server-Side Encryption with Customer-Provided Encryption Keys (SSE-C) - Amazon Simple Storage Service

Protecting Data Using Server-Side Encryption with Amazon S3-Managed Encryption Keys (SSE-S3) - Amazon Simple Storage Service

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost-effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance. To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.

The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewrites the table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows. This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.

The other options are not optimal for the following reasons:

A. VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.

B. VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

D. VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

1: Amazon Redshift VACUUM

2: Amazon Redshift Interleaved Sorting

3: Amazon Redshift ANALYZE

 The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.

AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entire table metadata. This reduces the query planning time and improves the query performance1.

Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to ‘project’, or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.

Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.

Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.

Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:

Improve query performance using AWS Glue partition indexes

Partition projection with Amazon Athena

Bucketing vs Partitioning

Columnar Storage Formats

S3DistCp

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.

Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.

Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.

Other options:

SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.

Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.

Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.

The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. References:

Using Apache Spark in Amazon Athena

Athena JDBC Driver

Spark SQL

Athena query settings

[Athena workgroups]

[Athena query editor]

 Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance. By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.

The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:

Choosing a data distribution style

Choosing a data sort key

Working with primary keys

Problem Analysis:

The company uses Kinesis Data Streams for both inventory management and reordering.

The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.

Duplicate records were observed in the inventory reordering system.

Key Considerations:

Kinesis streams are designed for durability but may produce duplicates under certain conditions.

Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.

Solution Analysis:

Option A: Network-Related Timeouts

If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.

Option B: High IteratorAgeMilliseconds

High iterator age suggests delays in processing but does not directly cause duplication.

Option C: Changes in Shards or Processors

Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.

Option D: AggregationEnabled Set to True

AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.

Option E: High max_records Value

A high max_records value increases batch size but does not lead to duplication.

Final Recommendation:

Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.

Amazon Kinesis Data Streams Best Practices

Kinesis Producer Library (KPL) Overview

Kinesis Client Library (KCL) Overview

 Amazon Redshift Serverless is a new feature of Amazon Redshift that enables you to run SQL queries on data in Amazon S3 without provisioning or managing any clusters. You can use Amazon Redshift Serverless to automatically process the analytics workload, as it scales up and down the compute resources based on the query demand, and charges you only for the resources consumed. This solution will meet the requirements with the least operational overhead, as it does not require the data engineer to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI, AWS SDK, or AWS Lambda functions12.

The other options are not optimal for the following reasons:

A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month. This option is not recommended, as it would still require the data engineer to create and delete a new Redshift provisioned cluster every month, which can incur additional costs and time. Moreover, this option would require the data engineer to use Amazon Step Functions to orchestrate the workflow of pausing and resuming the cluster, which can add complexity and overhead.

C. Use the AWS CLI to automatically process the analytics workload. This option is vague and does not specify how the AWS CLI is used to process the analytics workload. The AWS CLI can be used to run queries on data in Amazon S3 using Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these services has different features and benefits. Moreover, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to provision and configure some resources, such as Amazon EMR clusters or Amazon Athena workgroups.

D. Use AWS CloudFormation templates to automatically process the analytics workload. This option is also vague and does not specify how AWS CloudFormation templates are used to process the analytics workload. AWS CloudFormation is a service that lets you model and provision AWS resources using templates. You can use AWS CloudFormation templates to create and delete a Redshift provisioned cluster every month, or to create and configure other AWS resources, such as Amazon EMR, Amazon Athena, or Amazon Redshift Serverless. However, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to write and maintain the AWS CloudFormation templates, and to monitor the status and performance of the resources.

1: Amazon Redshift Serverless

2: Amazon Redshift Data API

Amazon Step Functions

AWS CLI

AWS CloudFormation

To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:

Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of 'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.

Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).

The best practice to restrict Amazon S3 access to specific VPCs is to use a bucket policy with a StringEquals or StringLike condition on aws:SourceVpc. This ensures only requests from a specified VPC are allowed.

IAM policies (option C) control who can access the resource but are not suitable alone to restrict by VPC.

Security Groups and NACLs (options B and D) do not apply to Amazon S3 because it is a global service and not VPC-bound.

“You can restrict access to your S3 bucket so that only requests coming from a specific VPC endpoint are allowed.”

Source: AWS Documentation – Amazon S3 Bucket Policies for VPC Endpoints

The FLEX execution class allows you to run AWS Glue jobs on spare compute capacity instead of dedicated hardware. This can reduce the cost of running non-urgent or non-time sensitive data integration workloads, such as testing and one-time data loads. The FLEX execution class is available for AWS Glue 3.0 Spark jobs. The other options are not as cost-effective as FLEX, because they either use dedicated resources (STANDARD) or do not affect the cost at all (Spot Instance type and GlueVersion). References:

Introducing AWS Glue Flex jobs: Cost savings on ETL workloads

Serverless Data Integration – AWS Glue Pricing

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 125)

Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.

Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns. S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.

Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.

Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage. Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

What Is AWS Lake Formation? - AWS Lake Formation

Using AWS Lake Formation with Amazon Athena - AWS Lake Formation

Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation

Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation

Using Bucket Policies and User Policies - Amazon Simple Storage Service

Apache Ranger

Apache Pig

What Is Amazon Redshift? - Amazon Redshift

 An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data.

The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it also charges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. References:

What is a data lake?

Choosing a data format for your data lake

Using the MERGE INTO command in Delta Lake

[AWS Lambda quotas]

[AWS Database Migration Service quotas]

Step 1: Understanding the Data Use Case

The company has data stored in an Amazon S3 bucket and needs to provide teams access for analysis, ensuring that PII data is not included in the analysis. The solution should be simple to implement and maintain, ensuring minimal operational overhead.

Step 2: Why Option D is Correct

Option D (AWS Glue DataBrew) allows you to visually prepare and transform data without needing to write code. By using a DataBrew job, the company can:

Automatically detect and separate PII data from non-PII data.

Store PII data in a second S3 bucket for security, while keeping the original S3 bucket clean for analysis.

This approach keeps operational overhead low by utilizing DataBrew's pre-built transformations and the easy-to-use interface for non-technical users. It also ensures compliance by separating sensitive PII data from the main dataset.

Step 3: Why Other Options Are Not Ideal

Option A (Amazon Macie) is a powerful tool for detecting sensitive data, but Macie doesn't inherently remove or mask PII. You would still need additional steps to clean the data after Macie identifies PII.

Option B (S3 Object Lambda with Amazon Comprehend) introduces more complexity by requiring custom logic at the point of data access. Amazon Comprehend can detect PII, but using S3 Object Lambda to filter data would involve more overhead.

Option C (Kinesis Data Firehose and Comprehend) is more suitable for real-time streaming data use cases rather than batch analysis. Setting up and managing a streaming solution like Kinesis adds unnecessary complexity.

Conclusion:

Using AWS Glue DataBrew provides a low-overhead, no-code solution to detect and separate PII data, ensuring the analysis teams only have access to non-sensitive data. This approach is simple, compliant, and easy to manage compared to other options.

To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.

Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.

Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.

 Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient. Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code. References:

AWS Lambda layers

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda

AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3. You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.

“Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort. Automate validation by triggering jobs through EventBridge.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

The Choice state type in AWS Step Functions is designed to perform branching logic, i.e., routing execution to different paths based on conditions in the input data.

“The Step Functions Choice state lets you branch the execution flow depending on values in the state’s input. This allows you to run different processing logic based on dynamic conditions like values in the input JSON.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This makes Choice the correct answer for content-driven conditional workflows.

The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use a manifest file.

Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.

Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.

In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.

Option B: "Select * from Employee where Region ID='North America' and Department ID=20;"This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.

Option C: "Select * from Employee where Department ID=20 and Region ID='North America';"This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.

Options A, D, and E are less optimal because they do not utilize the sort key as effectively:

Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.

Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.

Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.

In Amazon Athena, you can use CREATE TABLE AS SELECT with WITH NO DATA to create an empty copy of an existing table’s schema:

“The query CREATE TABLE new_table AS SELECT * FROM old_table WITH NO DATA; creates a new table with the same schema but without copying over the data.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This is the most efficient way to create an empty version of the existing table.

To identify the reason why the Step Functions state machine is not able to run the EMR jobs, the company should take the following steps:

Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. The state machine code should have an IAM role that allows it to invoke the EMR APIs, such as RunJobFlow, AddJobFlowSteps, and DescribeStep. The state machine code should also have IAM permissions to access the Amazon S3 buckets that the EMR jobs use as input and output locations. The company can use Access Analyzer for S3 to check the access policies and permissions of the S3 buckets12. Therefore, option B is correct.

Query the flow logs for the VPC. The flow logs can provide information about the network traffic to and from the EMR cluster that is launched in the VPC. The company can use the flow logs to determine whether the traffic that originates from the EMR cluster can successfully reach the data providers, such as Amazon RDS, Amazon Redshift, or other external sources. The company can also determine whether any security group that might be attached to the EMR cluster allows connections to the data source servers on the informed ports. The company can use Amazon VPC Flow Logs or Amazon CloudWatch Logs Insights to query the flow logs3 . Therefore, option D is correct.

Option A is incorrect because it suggests using AWS CloudFormation to automate the Step Functions state machine deployment. While this is a good practice to ensure consistency and repeatability of the deployment, it does not help to identify the reason why the state machine is not able to run the EMR jobs. Moreover, creating a step to pause the state machine during the EMR jobs that fail and wait for a human user to send approval through an email message is not a reliable way to troubleshoot the issue. The company should use the Step Functions console or API to monitor the execution history and status of the state machine, and use Amazon CloudWatch to view the logs and metrics of the EMR jobs .

Option C is incorrect because it suggests changing the AWS Step Functions state machine code to use Amazon EMR on EKS. Amazon EMR on EKS is a service that allows you to run EMR jobs on Amazon Elastic Kubernetes Service (Amazon EKS) clusters. While this service has some benefits, such as lower cost and faster execution time, it does not support all the features and integrations that EMR on EC2 does, such as EMR Notebooks, EMR Studio, and EMRFS. Therefore, changing the state machine code to use EMR on EKS may not be compatible with the existing data pipeline and may introduce new issues.

Option E is incorrect because it suggests checking the retry scenarios that the company configured for the EMR jobs. While this is a good practice to handle transient failures and errors, it does not help to identify the root cause of why the state machine is not able to run the EMR jobs. Moreover, increasing the number of seconds in the interval between each EMR task may not improve the success rate of the jobs, and may increase the execution time and cost of the state machine. Configuring an Amazon SNS topic to store the error messages may help to notify the company of any failures, but it does not provide enough information to troubleshoot the issue.

1: Manage an Amazon EMR Job - AWS Step Functions

2: Access Analyzer for S3 - Amazon Simple Storage Service

3: Working with Amazon EMR and VPC Flow Logs - Amazon EMR

[4]: Analyzing VPC Flow Logs with Amazon CloudWatch Logs Insights - Amazon Virtual Private Cloud

[5]: Monitor AWS Step Functions - AWS Step Functions

[6]: Monitor Amazon EMR clusters - Amazon EMR

[7]: Amazon EMR on Amazon EKS - Amazon EMR

The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.

Option A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.

Options B, C, and D are less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.

Problem Analysis:

Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.

Processing time is increasing due to the additional testing facilities.

The goal is to reduce processing time while using the existing AWS Glue framework.

Key Considerations:

AWS Glue offers the dynamic frame file-grouping feature, which consolidates small files into larger, more efficient datasets during processing.

Grouping smaller files reduces overhead and speeds up processing.

Solution Analysis:

Option A: Lambda for File Grouping

Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.

Option B: AWS Glue Dynamic Frame File-Grouping

This option directly addresses the issue by grouping small files during Glue job execution.

Minimizes data processing time with no extra overhead.

Option C: Redshift COPY Command

COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).

Option D: Amazon EMR

While EMR is powerful, replacing Glue with EMR increases operational complexity.

Final Recommendation:

Use AWS Glue dynamic frame file-grouping for optimized data ingestion and processing.

AWS Glue Dynamic Frames

Optimizing Glue Performance

 AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure.

AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.

AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.

Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:

AWS Glue Workflows

AWS Step Functions

AWS Glue Studio

Amazon MWAA

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Problem Analysis:

The pipeline processes compressed files in S3 and must support incremental data processing.

AWS Glue features must facilitate tracking progress to avoid reprocessing the same data.

Key Considerations:

Incremental data processing requires tracking which files or partitions have already been processed.

The solution must be automated and efficient for large-scale ETL jobs.

Solution Analysis:

Option A: Workflows

Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.

Option B: Triggers

Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.

Option C: Job Bookmarks

Job bookmarks track the state of the data that has been processed, enabling incremental processing.

Automatically skip files or partitions that were previously processed in Glue jobs.

Option D: Classifiers

Classifiers determine the schema of incoming data but do not handle incremental processing.

Final Recommendation:

Job bookmarks are specifically designed to enable incremental data processing in AWS Glue ETL pipelines.

AWS Glue Job Bookmarks Documentation

AWS Glue ETL Features

In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.

Option D: YourEnvironmentName-TaskTask logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails. These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.

Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.

Problem Analysis:

The company must log all user activities and connection activities in Amazon Redshift for security compliance.

Key Considerations:

Redshift supports audit logging, which can be configured to write logs to an S3 bucket.

S3 provides durable, scalable, and cost-effective storage for logs.

Solution Analysis:

Option A: S3 for Logging

Standard approach for storing Redshift logs.

Easy to set up and manage with minimal cost.

Option B: Amazon EFS

EFS is unnecessary for this use case and less cost-efficient than S3.

Option C: Aurora MySQL

Using a database to store logs increases complexity and cost.

Option D: EBS Volume

EBS is not a scalable option for log storage compared to S3.

Final Recommendation:

Enable Redshift audit logging and specify an S3 bucket as the destination.

Amazon Redshift Audit Logging

Storing Logs in Amazon S3

Apache Parquet is a columnar storage format that is much more space-efficient than row-based formats like CSV, especially for analytics workloads. Transforming data from CSV to Parquet significantly reduces storage costs and improves query performance. According to the study guide:

“Parquet is a columnar storage file format that is optimized for use with analytics workloads, providing efficient storage and fast query performance.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

By switching to Parquet, the company can reduce both storage size and retrieval times, making it the optimal choice for cost-effective data analysis.

Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.

On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.

Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.

The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format. References:

Amazon Athena

Choosing the Right Data Format

AWS Glue

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

 Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB offers two capacity modes for throughput capacity: provisioned and on-demand. In provisioned capacity mode, you specify the number of read and write capacity units per second that you expect your application to require. DynamoDB reserves the resources to meet your throughput needs with consistent performance. In on-demand capacity mode, you pay per request and DynamoDB scales the resources up and down automatically based on the actual workload. On-demand capacity mode is suitable for unpredictable workloads that can vary significantly over time1.

The solution that meets the requirements in the most cost-effective way is to use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times and lower capacity during off-peak times. This solution has the following advantages:

It allows you to optimize the cost and performance of your DynamoDB table by adjusting the provisioned capacity according to your predictable workload patterns. You can use scheduled scaling to specify the date and time for the scaling actions, and the new minimum and maximum capacity limits. For example, you can schedule higher capacity for every Monday morning and lower capacity for weekends2.

It enables you to take advantage of the lower cost per unit of provisioned capacity mode compared to on-demand capacity mode. Provisioned capacity mode charges a flat hourly rate for the capacity you reserve, regardless of how much you use. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode1.

It ensures that your application performs consistently during peak usage times by having enough capacity to handle the increased load. You can also use auto scaling to automatically adjust the provisioned capacity based on the actual utilization of your table, and set a target utilization percentage for your table or global secondary index. This way, you can avoid under-provisioning or over-provisioning your table2.

Option A is incorrect because it suggests increasing the provisioned capacity to the maximum capacity that is currently present during peak load times. This solution has the following disadvantages:

It wastes money by paying for unused capacity during off-peak times. If you provision the same high capacity for all times, regardless of the actual workload, you are over-provisioning your table and paying for resources that you don’t need1.

It does not account for possible changes in the workload patterns over time. If your peak load times increase or decrease in the future, you may need to manually adjust the provisioned capacity to match the new demand. This adds operational overhead and complexity to your application2.

Option B is incorrect because it suggests dividing the table into two tables and provisioning each table with half of the provisioned capacity of the original table. This solution has the following disadvantages:

It complicates the data model and the application logic by splitting the data into two separate tables. You need to ensure that the queries are evenly distributed across both tables, and that the data is consistent and synchronized between them. This adds extra development and maintenance effort to your application3.

It does not solve the problem of adjusting the provisioned capacity according to the workload patterns. You still need to manually or automatically scale the capacity of each table based on the actual utilization and demand. This may result in under-provisioning or over-provisioning your tables2.

Option D is incorrect because it suggests changing the capacity mode from provisioned to on-demand. This solution has the following disadvantages:

It may incur higher costs than provisioned capacity mode for predictable workloads. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode, as you can reserve the capacity you need at a lower rate1.

It may not provide consistent performance during peak usage times, as on-demand capacity mode may take some time to scale up the resources to meet the sudden increase in demand. On-demand capacity mode uses adaptive capacity to handle bursts of traffic, but it may not be able to handle very large spikes or sustained high throughput. In such cases, you may experience throttling or increased latency.

1: Choosing the right DynamoDB capacity mode - Amazon DynamoDB

2: Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

3: Best practices for designing and using partition keys effectively - Amazon DynamoDB

[4]: On-demand mode guidelines - Amazon DynamoDB

[5]: How to optimize Amazon DynamoDB costs - AWS Database Blog

[6]: DynamoDB adaptive capacity: How it works and how it helps - AWS Database Blog

[7]: Amazon DynamoDB pricing - Amazon Web Services (AWS)

Problem Analysis:

The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.

The goal is to implement quality checks with minimal implementation effort.

Key Considerations:

AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.

DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.

Solution Analysis:

Option A: SQL Transform

SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.

Option B: Evaluate Data Quality Transform + DQDL

AWS Glue’s built-in Evaluate Data Quality transform is designed for this use case.

Allows defining thresholds and rules in DQDL with minimal coding effort.

Option C: Custom Transform with PyDeequ

PyDeequ is a powerful library but adds unnecessary complexity compared to Glue’s native features.

Option D: Custom Transform with Great Expectations

Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.

Final Recommendation:

Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.

AWS Glue Data Quality

DQDL Syntax and Examples

AWS Glue Studio Documentation

AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.

Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.

The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. References:

AWS Step Functions

AWS Glue

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions

Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.

The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations and coordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. References:

AWS Glue

Amazon EventBridge

Using AWS Glue to run ETL jobs against non-native JDBC data sources

[AWS Lambda quotas]

[Amazon Kinesis Data Firehose quotas]

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

“RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.