This is the free Amazon DEA-C01 practice question bank —
160 of 310 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-08-06.
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Question 1
A media streaming company is moving its application to AWS. The company wants to avoid data quality issues in user engagement analytics. The company needs to be able to reprocess several days of historical data without duplicates and without interrupting current real-time processing.
Which solution will meet these requirements?
A. Configure Amazon Kinesis Data Streams with extended retention. Implement idempotent consumers with Amazon DynamoDB checkpointing. Use separate processing paths for replay data and real-time data.
B. Configure Amazon Kinesis Data Streams with standard retention. Implement idempotent consumers with Amazon RDS checkpointing. Use separate processing paths with conditional logic for replay data and real-time data.
C. Configure Amazon Kinesis Data Streams with standard retention. Implement idempotent consumers with Amazon DynamoDB checkpointing. Use shared processing paths with conditional logic for replay data and real-time data.
D. Configure Amazon Kinesis Data Streams with extended retention. Implement idempotent consumers with Amazon RDS checkpointing. Use shared processing paths for replay data and real-time data.
Show Answer
Correct Answer: A
Explanation: Extended retention in Amazon Kinesis Data Streams allows replay of several days of historical data. Idempotent consumers prevent duplicate processing during replays, and DynamoDB is a common low-latency store for checkpoints/state. Using separate processing paths for replay and live traffic avoids interrupting real-time processing while historical data is reprocessed.
Question 2
A global ecommerce company occasionally receives customer data files in its Amazon S3 data lake. The company needs to automatically detect new data and mask sensitive data before making the data available to the company's analytics team.
Which solution will meet these requirements with the LEAST operational overhead?
A. Configure Amazon S3 Event Notifications to detect the new data and trigger an AWS Glue job. Use Amazon Macie to detect and mask the sensitive data. Store the processed data in Amazon Redshift.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to detect incoming data. Use Amazon EMR workflows to detect and mask sensitive data. Store the processed data in Amazon S3.
C. Use Amazon Kinesis Data Streams to capture new data. Use Amazon Comprehend to detect and mask the sensitive data. Store the processed data in Amazon DynamoDB tables.
D. Use Amazon EventBridge to detect new data and run AWS Glue workflows. Use AWS Glue DataBrew to detect and mask the sensitive data. Store the processed data in an S3 bucket.
Show Answer
Correct Answer: D
Explanation: Amazon EventBridge can detect S3 object creation events with low operational overhead and trigger AWS Glue workflows. AWS Glue DataBrew provides built-in data profiling and transformations, including identifying and masking/redacting sensitive data, and the processed dataset can be written back to Amazon S3 for analytics. The other options rely on services that are not intended for this ETL use case (MSK/Kinesis), misuse Amazon Comprehend, or incorrectly suggest Amazon Macie performs data masking rather than discovery/classification.
Question 3
A company uses Amazon SageMaker AI for its machine learning (ML) workflows. The company is organized into several project groups that use sensitive data. The company needs to give the project groups the ability to discover available datasets across different AWS accounts. The solution must maintain access controls and track all data access for compliance purposes.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon SageMaker Assets to publish, discover, and request access to datasets through the asset catalog with approval workflows that track data access.
B. Set up Amazon SageMaker Feature Store with cross-account access policies to automatically share data between AWS accounts without requiring approval workflows.
C. Set up IAM roles for each project group with permissions to access all datasets across all AWS accounts. Use AWS CloudTrail to record data access activity.
D. Create separate Amazon SageMaker Studio domains for each project group with isolated environments and no ability to share data between domains.
Show Answer
Correct Answer: A
Explanation: Amazon SageMaker Assets (through the SageMaker catalog/Amazon DataZone integration) is designed to publish and discover datasets across accounts, supports requesting access with approval workflows, and provides governance and auditability for compliance. Feature Store is for serving ML features rather than governed dataset discovery, broad IAM access violates least privilege, and isolated Studio domains do not enable cross-account discovery.
Question 4
A company has an application that is deployed on AWS. The application uses Amazon Simple Notification Service (Amazon SNS) with multiple topics. The company's security team needs to be able to audit all Publish and PublishBatch API actions for all the SNS topics. The company's application team and security team must also be able to query the audit data. The company has already established an event data store in AWS CloudTrail Lake to collect all events.
Which solution will meet these requirements with the LEAST operational overhead?
A. Enable management events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
B. Enable management events for the SNS topics. Use CloudTrail Lake to query the audit data.
C. Enable data events for the SNS topics. Use CloudTrail Lake to query the audit data.
D. Enable data events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
Show Answer
Correct Answer: C
Explanation: Publish and PublishBatch on Amazon SNS are data-plane API operations, so they must be captured as CloudTrail data events rather than management events. Because the company already has a CloudTrail Lake event data store collecting events and both teams need to query the audit data, using CloudTrail Lake directly provides the required querying capability with the least operational overhead, avoiding the need to maintain Glue tables and Athena.
Question 5
A company uses AWS Step Functions to orchestrate a data pipeline. The company has configured the Step Functions logs to push to Amazon CloudWatch Logs when the log level is FATAL The company has enabled logs for all AWS services in the pipeline.
A state named "preprocessing" invokes an AWS Lambda function named "preprocessing." The Lambda function preprocesses data before proceeding to the next state. The company needs to find error details if an error occurs during the data preprocessing.
Which CloudWatch Logs log group should the company check to find the error details?
A. The Step Functions TaskFailed event in the /aws/vendedlogs/states log group
B. The AWS CloudTrail logs SendTaskFailure event in the CloudTrail/logs/preprocessing log group
C. The Lambda logs in the laws/lambda/preprocessing log group
D. The Step Functions TaskSucceeded event in the /aws/vendedlogs/states log group
Show Answer
Correct Answer: C
Explanation: The detailed execution errors, exception messages, stack traces, and application logs generated by a Lambda function are written to that function's CloudWatch Logs log group (/aws/lambda/<function-name>). Step Functions logs at the FATAL level primarily indicate that a task or execution failed, not the Lambda's internal error details. CloudTrail SendTaskFailure is for activity logging and is not where Lambda runtime errors are found.
Question 6
A data engineer needs to validate the quality of files that are uploaded to an Amazon S3 bucket every day. The files are in CSV and JSON formats and schema variations exist. The data engineer needs a repeatable process to monitor data quality metrics such as null values, format inconsistencies, and outliers. The process must provide reusable rule-based profiling and scale across multiple datasets with minimal manual effort or custom code.
Which solution will meet these requirements?
A. Use AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
B. Use AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
C. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.
D. Use AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.
Show Answer
Correct Answer: C
Explanation: AWS Glue DataBrew is designed for no-code data profiling and data quality analysis. It can profile CSV and JSON datasets, detect nulls, format inconsistencies, outliers, and other quality metrics, and supports reusable recipes and scheduled jobs across multiple datasets with minimal manual effort. The ETL-based Glue options require custom transformation logic and are not primarily intended for reusable rule-based profiling. Manual dashboard review alone does not satisfy the requirement for a repeatable automated process.
Question 7
A company needs a solution to process streaming data by using Apache Spark in a Kubernetes environment. The solution must support event-driven scaling and optimize resource utilization. The company needs to integrate the solution with existing Kubernetes infrastructure deployed on Amazon Elastic Kubernetes Service (Amazon EKS).
Which solution will meet these requirements with the LEAST operational overhead?
A. Deploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.
B. Use Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event-driven scaling of Spark applications.
C. Use Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.
D. Implement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.
Show Answer
Correct Answer: B
Explanation: Amazon EMR on Amazon EKS provides a managed Apache Spark runtime integrated with existing EKS clusters, reducing operational overhead compared to self-managed Spark. Combining it with Kubernetes Event-Driven Autoscaling (KEDA) enables event-driven scaling for streaming workloads while optimizing resource utilization. The other options either require self-managing Spark or rely on cluster/node scaling rather than event-driven application scaling.
Question 8
A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day.
The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
B. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
C. Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
D. Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS. Use Amazon Kendra for full-text search capabilities.
Show Answer
Correct Answer: B
Explanation: AWS Glue workflows and triggers can be orchestrated by Step Functions, but the key requirements are scheduled daily ETL for legal documents, event/time-based processing for frequently changing lawyer availability, and complex full-text search. Amazon OpenSearch Service is purpose-built for scalable full-text search with low operational overhead. RDS is not suitable for complex full-text search at this scale, and building a custom Lambda search solution increases operational burden. Kendra is aimed at intelligent enterprise search rather than serving as the primary indexed datastore for this ETL/search pipeline.
Question 9
A data engineer needs to deploy a serverless data pipeline. In the pipeline, CSV files are uploaded to an Amazon S3 bucket, which invokes an AWS Lambda function. The Lambda function transforms the CSV files to JSON format and stores the results in a second S3 bucket.
The data engineer has created an AWS Serverless Application Model (AWS SAM) template that includes the Lambda function. The data engineer wants to use AWS SAM for the pipeline deployment.
Which solution will package and deploy this serverless data pipeline?
A. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command to prepare the deployment package. Run the sam deploy --guided command to deploy the pipeline.
B. Run the sam deploy command directly with the --s3-bucket parameter to deploy the Lambda function code. Manually configure the S3 event trigger in the AWS Management Console.
C. Add the first S3 bucket to the SAM template. Run the sam package template to upload the Lambda function code to Amazon S3. Create an AWS CloudFormation stack from the packaged template. Configure event notifications manually.
D. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command followed by the aws cloudformation deploy command to deploy the pipeline.
Show Answer
Correct Answer: A
Explanation: AWS SAM should define all serverless resources, including the S3 bucket event source that triggers the Lambda function. The standard workflow is to run `sam build` to prepare the application and then `sam deploy --guided` (or `sam deploy`) to package, transform, and deploy the SAM application as a CloudFormation stack. The other options either require manual configuration of the event notification, use outdated/incomplete packaging steps, or bypass the normal SAM deployment workflow.
Question 10
A company stores objects in an Amazon S3 bucket. The company crawls the objects so that Amazon Athena can query the data.
A data engineer manually moved all objects from the partition with a path prefix of status=01 to the prefix status=02. The status=01 partition location is now empty. However, the status=01 partition location still appears in the AWS Glue Data Catalog metadata.
Which Athena command should the data engineer run to resolve the metadata discrepancy?
A. MSCK REPAIR TABLE
B. ALTER TABLE DROP PARTITION
C. ALTER TABLE SET TBLPROPERTIES
D. ALTER TABLE CHANGE COLUMN
Show Answer
Correct Answer: B
Explanation: MSCK REPAIR TABLE discovers new partitions but does not remove stale partition metadata. Because the objects were manually moved and the old partition location is now empty, the obsolete partition entry must be removed from the AWS Glue Data Catalog by dropping the partition. ALTER TABLE SET TBLPROPERTIES and CHANGE COLUMN are unrelated.
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