DEA-C01 practice questions 301–310

1. Question 301 · 313 in bank

   A company needs a solution that restricts access to Amazon S3 data and encrypts the data by using AWS managed keys. The solution must manage database credentials that an AWS Lambda function uses and must rotate the credentials automatically. Which solution will meet these requirements?

   • AUse S3 bucket policies to control access. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the data. Store the database credentials as Lambda environment variables.
   • BUse IAM policies to control access. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the data. Configure AWS Secrets Manager to store and automatically rotate the credentials by using a Lambda function.
   • CUse S3 ACLs to control access. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the data. Store the credentials in AWS Systems Manager Parameter Store and automatically rotate the credentials by using a Lambda function.
   • DUse IAM policies to control access. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the data. Store the credentials in AWS Systems Manager Parameter Store. Configure a scheduled Lambda function to rotate the credentials.

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   Answer: B

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2. Question 302 · 313 in bank

   A company needs to store semi-structured transactional data for an application in a database. The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds. Which solution will meet these requirements with the LEAST operational overhead?

   • AStore the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.
   • BStore the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.
   • CStore the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.
   • DStore the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.

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   Answer: D

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3. Question 303 · 313 in bank

   An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs. Which solution will meet these requirements?

   • AAdd additional permissions to the Lambda execution role.
   • BSet the log level to ERROR in the Lambda function code.
   • CConfigure the Lambda function to use the JSON log format.
   • DConfigure the Lambda function to send logs to a custom log group.

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   Answer: C

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4. Question 304 · 313 in bank

   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?

   • AThe Step Functions TaskFailed event in the /aws/vendedlogs/states log group
   • BThe AWS CloudTrail logs SendTaskFailure event in the CloudTrail/logs/preprocessing log group
   • CThe Lambda logs in the laws/lambda/preprocessing log group
   • DThe Step Functions TaskSucceeded event in the /aws/vendedlogs/states log group

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   Answer: C

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5. Question 305 · 313 in bank

   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?

   • AConfigure 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.
   • BConfigure 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.
   • CConfigure 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.
   • DConfigure 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.

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   Answer: A

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6. Question 306 · 313 in bank

   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?

   • AConfigure 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.
   • BUse 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.
   • CUse 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.
   • DUse 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.

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   Answer: D

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7. Question 307 · 313 in bank

   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?

   • AUse Amazon SageMaker Assets to publish, discover, and request access to datasets through the asset catalog with approval workflows that track data access.
   • BSet up Amazon SageMaker Feature Store with cross-account access policies to automatically share data between AWS accounts without requiring approval workflows.
   • CSet 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.
   • DCreate separate Amazon SageMaker Studio domains for each project group with isolated environments and no ability to share data between domains.

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   Answer: A

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8. Question 308 · 313 in bank

   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?

   • AEnable management events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
   • BEnable management events for the SNS topics. Use CloudTrail Lake to query the audit data.
   • CEnable data events for the SNS topics. Use CloudTrail Lake to query the audit data.
   • DEnable data events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.

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   Answer: C

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9. Question 309 · 313 in bank

   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?

   • AUse AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
   • BUse AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
   • CUse 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.
   • DUse AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.

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   Answer: C

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10. Question 310 · 313 in bank

    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?

    • ADeploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.
    • BUse Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event-driven scaling of Spark applications.
    • CUse Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.
    • DImplement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.

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    Answer: B

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DEA-C01 practice, question 301. 313 questions available.