AWS DEA-C01 Practice Questions with Explanations

Q: A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that

D. When AWS Glue attempts to access S3 through a VPC gateway endpoint, the VPC route table must contain the appropriate routes to enable traffic to reach the endpoint. The route table needs both inbound and outbound routes configured for the S3 VPC gateway endpoint so that the Glue job's traffic is properly directed through the endpoint. This is a routing configuration issue at the VPC level, not a security group, bucket policy, or application code issue.

Q: A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts. Which

B. AWS Lake Formation provides built-in row-level security features that allow enforcement of access policies based on attributes like country. By registering the S3 bucket as a data lake location in Lake Formation and configuring row-level security, analysts can access the same table but only see rows relevant to their country. This requires minimal operational effort compared to creating separate tables, managing multiple regions, or building custom Redshift views and IAM roles.

Q: A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to m

A. AWS Data Exchange is purpose-built for accessing and integrating third-party datasets with minimal effort. It provides pre-packaged datasets that can be accessed via API calls and easily integrated into existing analytics platforms. This is the most straightforward solution with the least operational overhead compared to alternatives.

Q: A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions. The data engineer requires a less manual way to updat

B. Lambda layers allow packaging of custom Python scripts once and applying them to multiple Lambda functions. When the scripts are updated, the layer is updated once and all Lambda functions automatically use the new version. This eliminates manual updates to each individual function, providing a centralized, maintainable solution.

Q: A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the d

B. AWS Glue workflows are purpose-built for orchestrating AWS Glue jobs including crawlers and ETL tasks. They provide native integration with Glue components, built-in error handling, and are the most cost-effective option for this specific use case. Workflows eliminate the need for external orchestration tools and are optimized for Glue-based pipelines.

August 14, 2026

Free AWS DEA-C01 practice questions. 50 of them, each with the correct answer, a full explanation, and the reason every other option is wrong. These are real questions from the DEA-C01 exam, not paraphrases, and every explanation is written out rather than just marking the right letter.

They are drawn from the same bank as the full DEA-C01 pack, which has 366 questions in total.

Get the full DEA-C01 question bank (366 questions) →

DEA-C01 practice questions

Question 1

A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint. The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket. Which solution will meet this requirement?

A. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.
B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.
C. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.
D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

Show answer and explanation ▾

Correct answer: D

When AWS Glue attempts to access S3 through a VPC gateway endpoint, the VPC route table must contain the appropriate routes to enable traffic to reach the endpoint. The route table needs both inbound and outbound routes configured for the S3 VPC gateway endpoint so that the Glue job's traffic is properly directed through the endpoint. This is a routing configuration issue at the VPC level, not a security group, bucket policy, or application code issue.

Why the other options are wrong:

A. Security groups control traffic to EC2 instances; VPC gateway endpoints do not receive inbound traffic from security groups in the same way.
B. An S3 bucket policy grants permissions but does not resolve VPC endpoint routing issues that prevent connectivity.
C. The Glue connection details do not need a fully qualified domain name for S3 VPC gateway endpoints; routing tables are the issue.

Question 2

A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts. Which solution will meet these requirements with the LEAST operational effort?

A. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
C. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
D. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

Show answer and explanation ▾

Correct answer: B

AWS Lake Formation provides built-in row-level security features that allow enforcement of access policies based on attributes like country. By registering the S3 bucket as a data lake location in Lake Formation and configuring row-level security, analysts can access the same table but only see rows relevant to their country. This requires minimal operational effort compared to creating separate tables, managing multiple regions, or building custom Redshift views and IAM roles.

Why the other options are wrong:

A. Creating separate tables for each country is operationally intensive and does not scale well as countries are added.
C. Moving data to multiple regions introduces significant complexity, cost, and operational overhead for a data governance problem.
D. Using Amazon Redshift with separate views and IAM roles requires more manual management and operational effort than Lake Formation's built-in security features.

Question 3

A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to minimize the effort and time required to incorporate third-party datasets. Which solution will meet these requirements with the LEAST operational overhead?

A. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
B. Use API calls to access and integrate third-party datasets from AWS DataSync.
C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Show answer and explanation ▾

Correct answer: A

AWS Data Exchange is purpose-built for accessing and integrating third-party datasets with minimal effort. It provides pre-packaged datasets that can be accessed via API calls and easily integrated into existing analytics platforms. This is the most straightforward solution with the least operational overhead compared to alternatives.

Why the other options are wrong:

B. AWS DataSync is designed for data transfer and synchronization between storage systems, not for accessing third-party datasets.
C. Amazon Kinesis Data Streams is a real-time streaming service, not a third-party dataset source; AWS CodeCommit is for version control.
D. Kinesis Data Streams is not designed for dataset integration; Amazon ECR is a container registry, not a third-party dataset source.

Question 4

A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions. The data engineer requires a less manual way to update the Lambda functions. Which solution will meet this requirement?

A. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.
B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
C. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
D. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.

Show answer and explanation ▾

Correct answer: B

Lambda layers allow packaging of custom Python scripts once and applying them to multiple Lambda functions. When the scripts are updated, the layer is updated once and all Lambda functions automatically use the new version. This eliminates manual updates to each individual function, providing a centralized, maintainable solution.

Why the other options are wrong:

A. Storing pointers in the execution context requires manual management and does not provide centralized versioning.
C. Using environment variables to store pointers still requires manual updates to each Lambda function configuration.
D. Lambda aliases are used for version management of the functions themselves, not for sharing code across functions.

Question 5

A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline. Which AWS service or feature will meet these requirements MOST cost-effectively?

A. AWS Step Functions
B. AWS Glue workflows
C. AWS Glue Studio
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Show answer and explanation ▾

Correct answer: B

AWS Glue workflows are purpose-built for orchestrating AWS Glue jobs including crawlers and ETL tasks. They provide native integration with Glue components, built-in error handling, and are the most cost-effective option for this specific use case. Workflows eliminate the need for external orchestration tools and are optimized for Glue-based pipelines.

Why the other options are wrong:

A. AWS Step Functions is a general-purpose orchestration service with additional costs compared to Glue's native workflow capabilities.
C. AWS Glue Studio is a visual design tool for creating jobs, not an orchestration service.
D. Amazon MWAA is a managed Apache Airflow service that adds operational overhead and cost for a task that Glue workflows handle natively.

Question 6

A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application. Which solution will meet these requirements with the LEAST operational overhead?

A. Establish WebSocket connections to Amazon Redshift.
B. Use the Amazon Redshift Data API.
C. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

Show answer and explanation ▾

Correct answer: B

The Amazon Redshift Data API allows applications to execute SQL queries without managing connections or database drivers, making it ideal for web-based applications. It requires minimal setup, handles authentication through IAM, and supports asynchronous query execution. This approach minimizes operational overhead compared to managing persistent connections or alternative data retrieval methods.

Why the other options are wrong:

A. WebSocket connections require custom implementation and management of persistent connections, adding operational complexity.
C. JDBC connections require managing driver versions, connection pools, and network connectivity, adding operational overhead.
D. Copying data to S3 and using S3 Select introduces latency and does not provide true real-time query capabilities on live Redshift data.

Question 7

A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account. Which solution will meet these requirements?

A. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
B. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
C. Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.
D. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Show answer and explanation ▾

Correct answer: B

Athena workgroups provide separation of query processes, query history, and cost tracking per workgroup. By creating a workgroup for each use case and applying IAM policies that use tags to control permissions, the company can enforce fine-grained access control while separating concerns among users, teams, and applications within the same account. This is the most direct and purpose-built solution.

Why the other options are wrong:

A. Creating separate S3 buckets per use case is operationally complex and does not separate Athena query history or processes.
C. IAM roles alone do not provide the workgroup-level separation of query processes and query history that Athena workgroups offer.
D. AWS Glue Data Catalog resource policies control catalog access but do not provide Athena-specific controls for query history, processes, or cost separation.

Question 8

A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time. Which solution will run the Glue jobs in the MOST cost-effective way?

A. Choose the FLEX execution class in the Glue job properties.
B. Use the Spot Instance type in Glue job properties.
C. Choose the STANDARD execution class in the Glue job properties.
D. Choose the latest version in the GlueVersion field in the Glue job properties.

Show answer and explanation ▾

Correct answer: A

The FLEX execution class is designed for non-time-sensitive workloads and uses spare capacity at a lower cost than STANDARD. Since the data engineer does not require the jobs to run at a specific time, FLEX execution class is the most cost-effective option. It provides significant cost savings for flexible, intermittent workloads by utilizing idle cluster resources.

Why the other options are wrong:

B. Spot Instances still incur compute costs and are not specifically designed for non- time-sensitive Glue jobs.
C. STANDARD execution class is the default and more expensive option for time- sensitive workloads.
D. Glue version selection does not directly impact cost-effectiveness for scheduling flexibility.

Question 9

A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket. Which solution will meet these requirements with the LEAST operational overhead?

A. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
B. Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
C. Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
D. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.

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Correct answer: A

Option A provides the most direct and operationally efficient solution by using S3 event notifications with object creation events filtered by .csv suffix, directly invoking the Lambda function. This requires no intermediate services and automatically triggers the Lambda function whenever a .csv file is uploaded. It is the simplest architecture with minimal overhead.

Why the other options are wrong:

B. Using object tagging requires users to manually tag files, adding operational complexity and potential for human error.
C. The s3:* event type is overly broad and would trigger on all S3 events, not just uploads, wasting resources.
D. Adding SNS as an intermediary introduces unnecessary latency and operational overhead compared to direct Lambda invocation.

Question 10

A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column. Which solution will MOST speed up the Athena query performance?

A. Change the data format from .csv to JSON format. Apply Snappy compression.
B. Compress the .csv files by using Snappy compression.
C. Change the data format from .csv to Apache Parquet. Apply Snappy compression.
D. Compress the .csv files by using gzip compression.

Show answer and explanation ▾

Correct answer: C

Apache Parquet is a columnar storage format optimized for analytical queries and selective column access. Since users primarily query specific columns, Parquet's columnar nature allows Athena to read only the required columns, significantly improving query speed. Combined with Snappy compression, it provides both fast query performance and reduced storage/bandwidth costs compared to uncompressed CSV.

Why the other options are wrong:

A. JSON is row-oriented and does not provide the columnar benefits needed for selective column queries.
B. Snappy compression on CSV files still leaves the row-oriented format inefficient for column-specific queries.
D. Gzip compression is effective for storage but does not address the inefficiency of querying specific columns from a row-oriented format.

Question 11

A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket. The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility. Which solution will meet these requirements with the LOWEST latency?

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

Show answer and explanation ▾

Correct answer: A

Amazon Managed Service for Apache Flink processes streaming data in real-time with minimal latency directly from Kinesis Data Streams, and Timestream is a time-series database optimized for real-time data visualization. This architecture eliminates the batch delays of S3 and Lambda, and directly streams data without intermediate storage. Flink with a Timestream connector provides the lowest latency for real-time dashboard updates.

Why the other options are wrong:

B. S3 notification and Lambda introduce processing latency and require batch operations, making this slower than streaming.
C. Using Firehose as an intermediary instead of direct Flink streaming adds unnecessary buffering and latency.
D. AWS Glue bookmarks are designed for batch processing, not real-time streaming, introducing significant latency.

Question 12

A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data. The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog. Which solution will meet these requirements?

A. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
B. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
C. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.
D. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.

Show answer and explanation ▾

Correct answer: B

The correct configuration requires the AWSGlueServiceRole (which includes necessary S3 and Glue permissions), scheduling the crawler to run daily, and specifying a database name for output to the Data Catalog. The crawler stores metadata in the Data Catalog database, not in S3. AWSGlueServiceRole is the proper managed role with appropriate permissions for Glue operations.

Why the other options are wrong:

A. AmazonS3FullAccess is overly permissive and not the recommended role; also outputs to S3 path instead of Data Catalog database.
C. AmazonS3FullAccess is not the proper Glue role; DPUs are for Glue job execution, not crawler scheduling.
D. Outputs to S3 path instead of Data Catalog database, which does not make data accessible through the Data Catalog as required.

Question 13

A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded. A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB. How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
D. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

Show answer and explanation ▾

Correct answer: B

The Redshift Data API can publish events to Amazon EventBridge, which can be configured with rules to trigger Lambda functions based on those events. This is the proper AWS-native integration pattern for connecting Redshift operations to downstream Lambda functions. EventBridge provides event-driven automation that responds to Redshift load completion events.

Why the other options are wrong:

A. Using a second Lambda function with CloudWatch events is unnecessarily complex and does not leverage Redshift's native event capabilities.
C. The Redshift Data API does not natively publish to SQS; this approach lacks native integration support.
D. CloudTrail events are audit logs, not real-time operational events, and would introduce unnecessary delay and complexity.

Question 14

A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically. Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A. AWS DataSync
B. AWS Glue
C. AWS Direct Connect
D. Amazon S3 Transfer Acceleration

Show answer and explanation ▾

Correct answer: A

AWS DataSync is purpose-built for automated, scheduled data transfer between on- premises systems and AWS. It efficiently handles incremental changes (5% daily updates), supports multiple file formats natively, and provides automated scheduling capabilities. DataSync includes built-in data validation, encryption, and bandwidth optimization, making it the most operationally efficient solution for this use case.

Why the other options are wrong:

B. AWS Glue is designed for ETL data transformation, not efficient bulk data transfer and synchronization.
C. Direct Connect is a network connectivity service, not a data transfer service, and would still require additional tools for automation.
D. S3 Transfer Acceleration optimizes upload speed but does not provide scheduling, automation, or incremental sync capabilities.

Question 15

A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently. The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database. Which AWS service should the company use to meet these requirements?

A. AWS Lambda
B. AWS Database Migration Service (AWS DMS)
C. AWS Direct Connect
D. AWS DataSync

Show answer and explanation ▾

Correct answer: B

AWS Database Migration Service (DMS) is specifically designed for cost-effective database migrations from on-premises to AWS. It supports SQL Server migrations with minimal downtime through continuous replication and change data capture. DMS is more cost-effective than direct connections and provides managed migration capabilities without requiring application changes.

Why the other options are wrong:

A. AWS Lambda is not designed for large-scale database migrations and would require custom code management.
C. AWS Direct Connect provides network connectivity but is not a migration service and does not reduce transfer costs.
D. AWS DataSync is optimized for file-level data transfer, not structured database migration with minimal downtime.

Question 16

A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling. Which solution will meet this requirement?

A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
C. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Show answer and explanation ▾

Correct answer: B

Concurrency scaling in Amazon Redshift is configured at the workload management (WLM) queue level within the provisioned cluster, not at the cluster creation level or in serverless workgroups. This setting allows Redshift to automatically provision additional resources to handle queued queries when the main cluster reaches capacity, scaling both read and write operations to meet demand.

Why the other options are wrong:

A. Concurrency scaling is not available for Redshift Serverless workgroups; it applies to provisioned clusters only.
C. Concurrency scaling is not configured during cluster creation; it is managed at the WLM queue level after cluster creation.
D. Concurrency scaling is not related to daily usage quotas; it is a WLM feature for handling query queues.

Question 17

A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options. The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS. Which extract, transform, and load (ETL) service will meet these requirements?

A. AWS Glue
B. Amazon EMR
C. AWS Lambda
D. Amazon Redshift

Show answer and explanation ▾

Correct answer: B

Amazon EMR is the only AWS service that natively supports Apache Pig, Oozie, Spark, HBase, and Flink-maintaining feature parity with the on-premises stack while delivering petabyte-scale processing. EMR provides the performance characteristics needed for existing workloads and allows seamless migration of these Apache ecosystem tools to AWS. Although not fully serverless, EMR balances operational efficiency with the requirement to maintain similar performance on familiar tools.

Why the other options are wrong:

A. AWS Glue does not support Pig, Oozie, HBase, or Flink, making it unsuitable for this multi-tool ecosystem.
C. Lambda cannot handle petabyte-scale processing or orchestrate complex Apache workloads within its execution constraints.
D. Redshift is an analytics warehouse, not an ETL service, and does not support the Apache ecosystem tools required.

Question 18

A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII. Which solution will meet this requirement with the LEAST operational effort?

A. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.
B. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
C. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
D. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.

Show answer and explanation ▾

Correct answer: B

Option B uses AWS Glue Studio's built-in Detect PII transform, which automatically identifies sensitive data without custom coding, then applies obfuscation through native transformations. Step Functions orchestrates the pipeline end-to-end with minimal configuration. This requires the least operational effort because Glue Studio provides purpose-built PII detection, eliminating the need to write custom Lambda functions or manage external tools. The native transform and orchestration minimize code and complexity.

Why the other options are wrong:

A. Requires custom Lambda code to identify and obfuscate PII, increasing development and operational effort compared to native Glue transforms.
C. Glue Data Quality rules are for validation and monitoring, not for obfuscation; this approach adds unnecessary complexity.
D. Ingesting into DynamoDB first adds unnecessary architectural steps and complexity without benefit; direct S3 ingestion is simpler.

Question 19

A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data. The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort. Which solution will meet these requirements with the LEAST operational overhead?

A. AWS Glue workflows
B. AWS Step Functions tasks
C. AWS Lambda functions
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

Show answer and explanation ▾

Correct answer: B

AWS Step Functions provides a serverless, declarative orchestration service specifically designed for complex workflows integrating multiple AWS services like Glue and EMR. Step Functions handles state management, error handling, retries, and parallelization automatically with minimal operational overhead. It integrates natively with both Glue and EMR and requires no infrastructure management, making it the least operationally burdensome solution for automated orchestration.

Why the other options are wrong:

A. Glue workflows are basic orchestration; Step Functions provides more advanced features and better integration across Glue and EMR.
C. Lambda requires manual orchestration and error handling logic, introducing significant operational complexity.
D. MWAA adds overhead through Airflow management; Step Functions is simpler and more AWS-native for this use case.

Question 20

A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class. A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year. The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability. Which solution will meet these requirements in the MOST cost-effective way?

A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

Show answer and explanation ▾

Correct answer: B

Option B transitions to S3 Standard-IA after 6 months (meets the infrequent access pattern and maintains high availability across multiple AZs) and then to S3 Glacier Flexible Retrieval after 2 years (for archival with acceptable retrieval times). Standard-IA provides the required high availability through multi-AZ replication, avoiding the single-AZ risk of One Zone-IA. Glacier Flexible Retrieval balances cost with the once or twice yearly access pattern better than Deep Archive for this use case.

Why the other options are wrong:

A. One Zone-IA sacrifices high availability by using only a single AZ, which contradicts the requirement.
C. Glacier Deep Archive is unnecessarily expensive for data accessed a few times per year; Glacier Flexible Retrieval is more cost-effective.
D. One Zone-IA fails the high availability requirement and Deep Archive is overkill for the access frequency.

Question 21

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks. The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster. The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster. Which solution will meet these requirements?

A. Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.
B. Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
C. Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
D. Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Show answer and explanation ▾

Correct answer: A

Redshift data sharing allows the sales team BI cluster to access data in the ETL cluster without duplicating data or impacting ETL performance. Data sharing is zero-copy, meaning queries against shared data do not consume compute resources from the source ETL cluster. The sales team can join their local data with shared data seamlessly. This meets all requirements: no interruption to critical ETL tasks, minimal resource consumption from the ETL cluster, and clean data access for the sales team.

Why the other options are wrong:

B. Direct access via materialized views still consumes ETL cluster resources and risks interruption to critical tasks.
C. Direct access via database views has the same issues; it doesn't isolate the ETL cluster from additional query load.
D. Weekly unload to S3 with Spectrum introduces staleness and extra complexity; data sharing provides real-time access without overhead.

Question 22

A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. Which solution will meet this requirement MOST cost-effectively?

A. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
B. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
C. Use Amazon Athena Federated Query to join the data from all data sources.
D. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Show answer and explanation ▾

Correct answer: C

Amazon Athena Federated Query is the most cost-effective solution for one-time analysis across multiple data sources. It allows querying data from DynamoDB, RDS, Redshift, and S3 without moving data, eliminating data transfer costs and the need to provision compute resources. You pay only for the queries you run.

Why the other options are wrong:

A. EMR provisioned clusters incur continuous costs even when idle, making them expensive for one-time jobs.
B. Copying data from multiple sources to S3 incurs significant data transfer costs and operational overhead.
D. Redshift Spectrum cannot query DynamoDB or RDS; it's limited to S3 and other external data sources accessible via external tables.

Question 23

A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
B. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
C. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

Show answer and explanation ▾

Correct answer: C

Creating an external schema in Redshift to map Kinesis Data Streams with an auto- refreshing materialized view provides the least operational overhead. This native Redshift feature eliminates the need for intermediate staging, separate tools, or manual refresh management. Auto-refresh ensures near real-time data availability automatically while using existing BI tools directly on the materialized view.

Why the other options are wrong:

A. Using S3 staging adds latency and operational complexity; COPY commands are batch operations, not true real-time.
B. Materialized views cannot be created directly on Kinesis streams without external schemas; this approach is not technically feasible.
D. Kinesis Data Firehose adds an additional service layer and staging step, increasing operational overhead compared to direct Redshift integration.

Question 24

A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file. Which Step Functions state should the data engineer use to meet these requirements?

A. Parallel state
B. Choice state
C. Map state
D. Wait state

Show answer and explanation ▾

Correct answer: C

The Map state in AWS Step Functions is specifically designed to iterate over a collection and run steps in parallel for each item. It's the native solution for distributing work across multiple items concurrently, making it perfect for applying transformations to a large collection of data files in parallel.

Why the other options are wrong:

A. Parallel state runs different branches concurrently but is not designed for iterating over collections of items.
B. Choice state makes conditional decisions about workflow paths; it doesn't perform parallel processing.
D. Wait state pauses execution for a specified duration; it has no role in parallel data processing.

Question 25

A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information. The data engineer must identify and remove duplicate information from the legacy application data. Which solution will meet these requirements with the LEAST operational overhead?

A. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
B. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
C. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
D. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Show answer and explanation ▾

Correct answer: B

AWS Glue's FindMatches ML transform is purpose-built for deduplication and handles fuzzy matching of duplicate records automatically. It requires minimal configuration and no custom code, representing the least operational overhead. FindMatches learns patterns and handles complex duplicates that simple drop_duplicates() functions cannot detect.

Why the other options are wrong:

A. Custom Python with Pandas requires writing and maintaining custom code; drop_duplicates() only handles exact duplicates, not fuzzy matches.
C. Custom Python with the dedupe library requires extensive tuning and training data preparation, adding operational complexity.
D. While using Glue reduces some overhead compared to custom Python, the dedupe library still requires significant configuration and tuning compared to FindMatches.

Question 26

A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway. Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.
B. Create an AWS Lambda Python function with provisioned concurrency.
C. Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).
D. Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.

Show answer and explanation ▾

Correct answer: B

AWS Lambda is the optimal choice for occasional invocations through API Gateway with minimal operational overhead. Lambda integrates natively with API Gateway, scales automatically, and requires no infrastructure management. Provisioned concurrency ensures the function is warm and ready to respond immediately, eliminating cold start latency concerns. This approach requires no server management, container orchestration, or scheduled warm-up logic.

Why the other options are wrong:

A. ECS requires managing a cluster, container definitions, and ongoing operational maintenance even for occasional invocations.
C. EKS adds unnecessary complexity with Kubernetes management for a simple occasional script execution.
D. Using EventBridge to invoke Lambda every 5 minutes with mock events wastes resources and adds complexity when provisioned concurrency is available.

Question 27

A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account. Which solution will meet these requirements?

A. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
C. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.

Show answer and explanation ▾

Correct answer: D

The Kinesis Data Stream must be created in the security account where the logs will be stored and analyzed. CloudWatch Logs in the production account needs permission to write to this stream via a trust policy. A subscription filter is created in the production account to direct CloudWatch Logs to the destination stream in the security account. This architecture allows logs to flow from production CloudWatch Logs to the security account's Kinesis stream for centralized analysis.

Why the other options are wrong:

A. Creating the destination stream in the production account defeats the purpose of centralizing security logs in the security account.
B. While the stream should be in the security account and the trust policy is correct, the subscription filter must be created in the production account where the logs originate, not the security account.
C. Creating the stream in the production account and granting cross-account permissions to the security account unnecessarily complicates the architecture and doesn't centralize log storage.

Question 28

A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. Which solution will capture the changed data MOST cost-effectively?

A. Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.
B. Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
C. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
D. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

Show answer and explanation ▾

Correct answer: C

Using an open source data lake format like Delta Lake, Apache Iceberg, or Apache Hudi is the most cost-effective approach for CDC with large terabyte-scale files. These formats support MERGE operations that efficiently identify and update only changed records without requiring database infrastructure. This approach leverages S3 storage directly, avoiding unnecessary compute costs from Lambda, RDS, or DMS while handling both small and large files efficiently.

Why the other options are wrong:

A. Lambda would need to load and compare entire terabyte-scale files in memory, exceeding Lambda's resource limits and incurring high compute costs.
B. RDS with DMS adds unnecessary database licensing and operational costs for a file- based CDC operation.
D. Aurora Serverless with DMS still incurs database costs and adds complexity compared to native data lake format operations on S3.

Question 29

A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant. Which solution will meet these requirements with the LEAST operational overhead?

A. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
C. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.
D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

Show answer and explanation ▾

Correct answer: D

Amazon Managed Service for Apache Flink (Kinesis Data Analytics) is purpose-built for real-time streaming analytics with windowed aggregations up to 30 minutes. It provides built-in fault tolerance, state management, and handles duplicate records with multiple aggregation types. This managed service eliminates operational overhead compared to building custom Lambda-based logic that would require complex state management, error handling, and window management.

Why the other options are wrong:

A. Lambda-based solutions lack native support for complex time-window aggregations and stateful processing required for 30-minute windows.
B. While Flink is correct, mentioning only duplicate handling doesn't emphasize the window-based aggregation capability as strongly.
C. Lambda with tumbling windows is complex and error-prone for stateful aggregations; Flink is the managed alternative.

Question 30

A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage. Which solution will meet these requirements with the LEAST operational overhead?

A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
B. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
C. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
D. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

Show answer and explanation ▾

Correct answer: C

EBS allows in-place volume type conversion from gp2 to gp3 without detaching the volume or stopping the instance, eliminating operational overhead and downtime. The change is applied online, and you can simultaneously adjust IOPS and throughput parameters. This is the least operationally intensive method for the upgrade.

Why the other options are wrong:

A. Creating snapshots and new volumes requires detaching old volumes and reattaching new ones, adding complexity and potential downtime.
B. Gradual data transfer between volumes is time-consuming and operationally complex.
D. AWS DataSync adds unnecessary complexity and cost for a simple volume type conversion that AWS handles natively.

Question 31

A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3. Which solution will meet these requirements in the MOST operationally efficient way?

A. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
B. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
C. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
D. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

Show answer and explanation ▾

Correct answer: C

Use a SQL query to create a view in the EC2 instance- based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day. Creating a view in the EC2-based SQL Server database and using AWS Glue to read and transform it is the most operationally efficient approach. Glue can crawl SQL Server views to populate the Data Catalog, and Glue jobs natively handle the data connection, transformation to Parquet, and S3 export in a single scheduled operation. This minimizes custom scripting and leverages Glue's native capabilities.

Why the other options are wrong:

A. While this works, using Glue directly on views without a crawler requires more manual configuration and lacks metadata catalog integration.
B. SQL Server Agent output to CSV followed by Lambda transformation adds operational complexity with extra S3 events and format conversion steps.
D. Lambda with JDBC requires custom connection pooling, timeout handling, and memory management for large data elements, increasing operational burden.

Question 32

A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues. Which table views should the data engineer use to meet this requirement?

A. STL_USAGE_CONTROL
B. STL_ALERT_EVENT_LOG
C. STL_QUERY_METRICS
D. STL_PLAN_INFO

Show answer and explanation ▾

Correct answer: B

STL_ALERT_EVENT_LOG is the system table that records alerts and anomalies when the Redshift query optimizer identifies conditions that might indicate performance issues. This table specifically captures optimizer-detected problems such as missing statistics, row estimator problems, and other performance concerns.

Why the other options are wrong:

A. STL_USAGE_CONTROL tracks resource quotas and limits, not query performance anomalies.
C. STL_QUERY_METRICS provides execution metrics and statistics but does not record optimizer-detected anomalies.
D. STL_PLAN_INFO contains query plan information but does not record performance anomaly alerts.

Question 33

A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file. Which solution will meet these requirements MOST cost-effectively?

A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
C. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

Show answer and explanation ▾

Correct answer: D

Apache Parquet is a columnar storage format that is ideal for analytical queries on a subset of columns. Since data analysts rarely query the entire file and only access one or two columns out of 15, Parquet's columnar structure allows Athena to read only the required columns, significantly reducing I/O costs and query latency compared to row- based formats like CSV or JSON. Avro is also columnar but less optimized for Athena queries than Parquet.

Why the other options are wrong:

A. CSV format requires reading all 15 columns even when querying just one or two, wasting I/O and increasing costs.
B. JSON format is row-based and also requires reading entire records, making it inefficient for selective column access.
C. While Avro is a good format, Parquet is more optimized for analytical queries with selective column access in Athena.

Question 34

A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated. A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data. Which solution will meet this requirement?

A. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.
B. Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.
C. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.
D. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.

Show answer and explanation ▾

Correct answer: C

Instance store volumes are ephemeral and deleted when instances terminate, so data on them cannot persist. The correct approach is to use an AMI backed by instance store (option C) but attach an additional Amazon EBS volume to store application data. EBS volumes persist independently of EC2 instance lifecycle, ensuring data remains available even after instance termination. This separates the root filesystem from the persistent data storage.

Why the other options are wrong:

A. Instance store volumes are deleted when the instance terminates, so data will not persist.
B. While EBS-backed AMIs persist the root volume, the default settings would delete the root EBS volume on termination unless explicitly configured otherwise, and this does not isolate application data.
D. Instance store volumes are still ephemeral and will not preserve data; attaching EBS as additional storage is unnecessary when the root is already EBS-backed.

Question 35

A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics. Which solution will give the company the ability to use Spark to access Athena?

A. Athena query settings
B. Athena workgroup
C. Athena data source
D. Athena query editor

Show answer and explanation ▾

Correct answer: B

Athena workgroups provide the ability to configure Athena to run Apache Spark applications in addition to SQL queries. A workgroup is a logical grouping of queries and compute resources where you can specify the engine (SQL or Spark), resource configurations, and other execution settings. This allows the company to use Spark instead of SQL for analytics while maintaining access to the same data.

Why the other options are wrong:

A. Athena query settings control performance and output options but do not enable Spark execution.
C. Athena data sources are used to connect to external data repositories and do not provide Spark capability.
D. The Athena query editor is a UI component for writing and executing queries but does not enable Spark support by itself.

Question 36

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01. A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket. Which solution will meet these requirements with the LEAST latency?

A. Schedule an AWS Glue crawler to run every morning.
B. Manually run the AWS Glue CreatePartition API twice each day.
C. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.
D. Run the MSCK REPAIR TABLE command from the AWS Glue console.

Show answer and explanation ▾

Correct answer: C

Using code that invokes the Boto3 AWS Glue create_partition API call directly provides the least latency because partitions are created immediately when data is written to S3, without waiting for scheduled crawlers or manual operations. This programmatic approach integrates partition creation into the data ingestion pipeline, ensuring the Data Catalog stays synchronized in real-time as new data arrives.

Why the other options are wrong:

A. Scheduled crawlers introduce latency as they only run at fixed intervals (morning schedule means missed partitions until next run).
B. Manual API calls twice daily still introduce significant latency and depend on manual intervention.
D. MSCK REPAIR TABLE scans S3 and discovers existing partitions but adds latency and is less efficient than direct API calls.

Question 37

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data. Which AWS service or feature will meet these requirements with the LEAST operational overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
B. Amazon AppFlow
C. AWS Glue Data Catalog
D. Amazon Kinesis

Show answer and explanation ▾

Correct answer: B

Amazon AppFlow is a fully managed service that enables secure data integration from SaaS applications directly to AWS services like S3 and Redshift with minimal operational overhead. It provides pre-built connectors for popular SaaS tools, scheduled or event- driven data flows, and automatic data transformation capabilities, eliminating the need for custom integration code or infrastructure management.

Why the other options are wrong:

A. Amazon MSK is for streaming data from Kafka sources and adds operational complexity for SaaS integration.
C. AWS Glue Data Catalog is a metadata repository; it does not ingest data from SaaS applications.
D. Amazon Kinesis is for real-time streaming but requires custom integration code and more operational overhead than AppFlow for SaaS data sources.

Question 38

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue. The data engineer's original query is as follows: • SELECT product_name, sum(sales_amount) FROM sales_data • WHERE year = 2023 • GROUP BY product_name How should the data engineer modify the Athena query to meet these requirements?

A. Replace sum(sales_amount) with count(*) for the aggregation.
B. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.
C. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
D. Remove the GROUP BY clause.

Show answer and explanation ▾

Correct answer: B

The issue is likely that the WHERE clause comparison year = 2023 is comparing a date/timestamp column to an integer, causing a type mismatch. Using EXTRACT(YEAR FROM sales_date) = 2023 properly extracts the year from the date column and compares it as an integer, ensuring all products with 2023 sales dates are returned. This resolves data type compatibility issues.

Why the other options are wrong:

A. Using COUNT(*) instead of SUM(sales_amount) changes the aggregation logic and does not address missing results.
C. Adding HAVING sum(sales_amount) > 0 filters results but does not address the underlying issue of missing products.
D. Removing GROUP BY would return incorrect results by collapsing all rows into a single row.

Question 39

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe. Write a SQL SELECT statement on the dataframe to query the required column.
B. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Show answer and explanation ▾

Correct answer: B

S3 Select is purpose-built for querying specific columns from objects in S3 without loading the entire dataset into memory. For a one-time task requiring minimal operational overhead, S3 Select with a SQL SELECT statement is the most efficient solution-it requires no infrastructure setup, no function deployment, and no crawler configuration. It directly queries the Parquet file and returns only the requested column, making it the least overhead option.

Why the other options are wrong:

A. Lambda with pandas requires writing and managing function code, installing dependencies, and handling data loading inefficiently when only one column is needed.
C. DataBrew is designed for data preparation workflows and requires project setup, making it overkill for a simple one-time column extraction.
D. Crawlers and Athena require metadata cataloging setup, Glue job execution, and ongoing resource management-significantly more overhead than S3 Select.

Question 40

A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views. Which solution will meet this requirement with the LEAST effort?

A. Use Apache Airflow to refresh the materialized views.
B. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
C. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
D. Use an AWS Glue workflow to refresh the materialized views.

Show answer and explanation ▾

Correct answer: C

The Redshift query editor v2 has built-in automation capabilities to schedule materialized view refreshes directly within the Redshift service with minimal configuration. This is a native Redshift feature requiring no external orchestration tools, making it the least effort solution for automating refresh schedules.

Why the other options are wrong:

A. Apache Airflow requires separate infrastructure setup and maintenance even when using managed versions.
B. Lambda UDFs within Redshift are not the standard approach for scheduling; they require additional orchestration outside Redshift to execute on a schedule.
D. AWS Glue workflows add unnecessary complexity and require workflow configuration when the task can be handled natively within Redshift.

Question 41

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services. Which solution will meet these requirements with the LEAST management overhead?

A. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
B. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
C. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
D. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

Show answer and explanation ▾

Correct answer: A

AWS Step Functions with a state machine provides serverless, managed orchestration specifically designed for AWS service integration. It requires minimal setup-just defining the state machine to run Lambda followed by Glue Job-and AWS handles all scaling and management. No infrastructure provisioning or third-party tools are needed.

Why the other options are wrong:

B. Apache Airflow on EC2 requires managing infrastructure, maintaining the Airflow service, and handling updates and patches.
C. AWS Glue workflows are limited and not as flexible as Step Functions for complex orchestration scenarios.
D. Apache Airflow on EKS requires Kubernetes infrastructure management, which adds significant operational overhead compared to Step Functions.

Question 42

A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3. The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Show answer and explanation ▾

Correct answer: B

AWS Glue Data Catalog is a purpose-built metadata repository integrated across AWS services. Glue crawlers automatically discover and update metadata from structured (RDS, Redshift) and semi-structured sources (S3 JSON, XML files), detecting schema changes automatically. Scheduling crawlers to run periodically provides fully managed, least-overhead solution without custom Lambda development.

Why the other options are wrong:

A. Aurora requires custom Lambda function development and management to gather metadata from multiple heterogeneous sources, adding operational complexity.
C. DynamoDB is not a suitable metadata repository and requires the same custom Lambda approach as Aurora, with additional schema management challenges.
D. This hybrid approach requiring manual schema extraction for relational sources is less automatic than using crawlers for all sources, requiring more manual effort and maintenance.

Question 43

A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends. The company must ensure that the application performs consistently during peak usage times. Which solution will meet these requirements in the MOST cost-effective way?

A. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
B. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
C. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.
D. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

Show answer and explanation ▾

Correct answer: C

Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off- peak times. AWS Application Auto Scaling supports scheduled scaling for DynamoDB, allowing the company to automatically provision higher capacity on Monday mornings and lower capacity during weekends. This matches the predictable schedule exactly, ensuring consistent performance during peaks while minimizing costs during low-usage periods- more cost-effective than maintaining peak capacity constantly.

Why the other options are wrong:

A. Provisioning to maximum peak capacity all the time wastes money on unused capacity during low-usage periods like weekends.
B. Splitting tables adds complexity without solving the core scaling issue and doesn't optimize for predictable scheduled patterns.
D. On-demand mode is less cost-effective than scheduled provisioning for predictable, recurring workload patterns with known peak times.

Question 44

A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution. The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog. Which solution will meet these requirements MOST cost-effectively?

A. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.
B. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
C. Configure an external Hive metastore in Amazon EMR. Migrate the existing on- premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
D. Configure a new Hive metastore in Amazon EMR. Migrate the existing on- premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.

Show answer and explanation ▾

Correct answer: B

Using AWS Glue Data Catalog as an external data catalog for EMR provides a serverless, managed metadata solution that integrates seamlessly with EMR. The company migrates the Hive metastore to EMR and uses Glue Data Catalog as the persistent, serverless metadata repository. This avoids managing a separate metastore infrastructure while maintaining Hadoop compatibility.

Why the other options are wrong:

A. Migrating Hive metastore to S3 and using Glue crawlers is unnecessarily indirect and creates a non-standard architecture.
C. Aurora MySQL requires managing a database instance, which is not serverless and contradicts the serverless requirement.
D. Using EMR-hosted metastore means managing persistent database infrastructure, which is not a serverless solution.

Question 45

A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution. A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations. The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes. Which solution will meet these requirements?

A. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
B. Change the distribution key to the table column that has the largest dimension.
C. Upgrade the reserved node from ra3.4xlarge to ra3.16xlarge.
D. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Show answer and explanation ▾

Correct answer: B

The uneven CPU load across nodes indicates poor distribution of data based on the current distribution key. Changing the distribution key to a column with larger cardinality or better selectivity ensures data is distributed more evenly across all five nodes, balancing the workload without changing node count or hardware specifications.

Why the other options are wrong:

A. Sort keys affect query performance and row ordering but do not influence how data is distributed across nodes.
C. Upgrading node size is unnecessary when the issue is data distribution imbalance, and it increases costs without solving the core problem.
D. Primary keys do not control data distribution in Redshift; only the distribution key determines which node stores which rows.

Question 46

A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data. Which solution will meet these requirements MOST cost-effectively?

A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
B. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.
C. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.
D. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

Show answer and explanation ▾

Correct answer: A

AWS Glue Data Catalog combined with AWS Glue Schema Registry is the most cost- effective serverless solution for managing evolving JSON data structures. The Schema Registry handles schema evolution automatically, the Data Catalog provides indexing, and the orchestration into Redshift Serverless (also serverless) keeps costs minimal by paying only for what's used. No infrastructure provisioning is required.

Why the other options are wrong:

B. Amazon Redshift provisioned cluster requires ongoing per-node costs regardless of utilization, and Spectrum adds complexity without the flexibility of a pure data catalog approach.
C. While Athena is serverless and cost-effective for querying, it doesn't create a managed data catalog with schema management capabilities for evolving structures.
D. This combines Glue Data Catalog with Lambda UDFs and Step Functions, adding unnecessary complexity and costs compared to the simpler integrated solution in option

Question 47

A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region. Which solution will meet this requirement with the LEAST operational effort?

A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.
D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

Show answer and explanation ▾

Correct answer: D

AWS CloudTrail with data events is the native AWS solution for logging S3 object-level operations like writes. Creating a trail configured for data events on the transactions S3 bucket with write-only filtering and specifying the logs S3 bucket as the destination provides comprehensive logging with minimal operational overhead. CloudTrail is purpose-built for audit logging and requires no custom code or additional services.

Why the other options are wrong:

A. Requires Lambda code development and adds unnecessary complexity with Kinesis Data Firehose when CloudTrail handles this natively.
B. Management events in CloudTrail track API calls but not the actual S3 object-level writes; data events are required for object operations.
C. Requires custom Lambda function development and ongoing maintenance, whereas CloudTrail provides this functionality out-of-the-box.

Question 48

A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository. Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon EMR and Apache Ranger.
B. Use a Hive metastore on an EMR cluster.
C. Use the AWS Glue Data Catalog.
D. Use a metastore on an Amazon RDS for MySQL DB instance.

Show answer and explanation ▾

Correct answer: C

AWS Glue Data Catalog is the purpose-built AWS service for managing metadata centrally and is fully integrated with both Amazon EMR and Amazon Athena. It can import Hive metastore metadata directly and provides a unified metadata repository with minimal development effort. Users can query through Athena and EMR without additional configuration.

Why the other options are wrong:

A. Apache Ranger is for access control, not metadata repository management.
B. A Hive metastore on EMR is distributed and not centralized, defeating the requirement for a central repository.
D. RDS for MySQL requires custom schema design and development effort to build a metadata repository, lacking native integration with Athena and EMR.

Question 49

A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
C. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
D. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

Show answer and explanation ▾

Correct answer: D

AWS Lake Formation provides fine-grained access control with both row-level and column-level permissions natively integrated with S3, Athena, Redshift Spectrum, and EMR. It is specifically designed for data lake scenarios and manages permissions centrally with minimal operational overhead compared to managing policies manually or through third-party tools.

Why the other options are wrong:

A. S3 access policies cannot enforce row-level or column-level restrictions; they operate at object level only.
B. Apache Ranger requires EMR cluster management and only works with EMR/Hive, not Athena or Redshift Spectrum directly.
C. Redshift is a data warehouse, not optimal for data lake storage; Redshift security policies don't provide the flexibility needed for multi-tool access patterns.

Question 50

A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions. The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column. Which Amazon Redshift command will meet these requirements?

A. VACUUM FULL Orders
B. VACUUM DELETE ONLY Orders
C. VACUUM REINDEX Orders
D. VACUUM SORT ONLY Orders

Show answer and explanation ▾

Correct answer: C

VACUUM REINDEX is the appropriate command for reclaiming disk space and re-sorting an interleaved sort key. It performs both the space reclamation that regular VACUUM provides and reindexes the interleaved sort key column, which optimizes for future queries on that Region column. This meets both requirements: freeing storage and analyzing the sort key.

Why the other options are wrong:

A. VACUUM FULL reclaims space but does not reindex interleaved sort keys; it's also slower than other options.
B. VACUUM DELETE ONLY only reclaims space from deleted rows but does not address sort key analysis or optimization.
D. VACUUM SORT ONLY is not a valid Redshift command; the correct command for sort key optimization is VACUUM REINDEX.

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