DEA-C01 practice questions 291–300

1. Question 291 · 313 in bank

   A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.complete_orders_history, which contains 100 columns. The query must return all columns except columns named companyId and unique_system_id. Which Amazon Redshift SQL statement will meet this requirement?

   • A//IMG//
   • B//IMG//
   • C//IMG//
   • D//IMG//

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   Answer: A

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2. Question 292 · 313 in bank

   A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions. Which solution will meet this requirement?

   • AEnable versioning on the S3 bucket and configure MFA delete.
   • BConfigure an S3 bucket policy rule that denies the creation of S3 delete markers.
   • CCreate an S3 Lifecycle rule that moves deleted files to S3 Glacier Deep Archive.
   • DSet up AWS Config remediation actions to prevent users from deleting S3 objects.

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   Answer: A

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3. Question 293 · 313 in bank

   A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%. Which component of an AWS Glue Data Quality rule will meet these requirements?

   • AUniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
   • BColumnValues "Email” matches "[%@%.%]" with a threshold set to > 0.1
   • CColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
   • DUniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1

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   Answer: C

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4. Question 294 · 313 in bank

   A company is building data processing pipelines by using AWS Glue. The pipelines access data stored in Amazon S3. The company has organized the data into folders with prefixes that represent different classification levels. The company needs to restrict AWS Glue jobs to access only specific prefixes based on the data classification. The company must also restrict access to business hours (9 AM to 5 PM). Which elements must the company include in a custom IAM policy to meet these requirements?

   • AA Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the $util.time variable with TimeGreaterThan and TimeLessThan operators
   • BA Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the aws:CurrentTime condition key with DateGreaterThan and DateLessThan operators
   • CA Condition element that uses the s3:prefix condition key to restrict folder access and aws:CurrentTime with DateGreaterThanEquals and DateLessThanEquals to restrict hours of operation
   • DA Condition element that uses the s3:ResourceAccount condition key to restrict bucket access and a Deny statement that applies outside of business hours

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   Answer: B

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5. Question 295 · 313 in bank

   A company needs to collect logs for an Amazon RDS for MySQL database and make the logs available for audits. The logs must track each user that modifies data in the database or makes changes to the database instance. Which solution will meet these requirements?

   • AEnable Amazon CloudWatch Logs. Create metric filters to monitor database changes and instance-level changes. Configure automated notification systems to send near real-time alerts for suspicious database operations.
   • BConfigure an Amazon EventBridge rule to monitor database activity. Create an AWS Lambda function to process EventBridge events and store them in Amazon OpenSearch Service.
   • CConfigure AWS CloudTrail to log API calls. Use Amazon CloudWatch Logs for basic monitoring. Use IAM policies to control access to the logs. Set up scheduled reporting for log audits.
   • DEnable and configure native Amazon RDS database audit logging. Enable Amazon CloudWatch Logs. Configure metric filters and alarms. Configure AWS CloudTrail audit logging.

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   Answer: D

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6. Question 296 · 313 in bank

   A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation. Which solution will meet these requirements with the LEAST operational overhead?

   • ACreate an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
   • BUse AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.
   • CDeploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.
   • DUse AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.

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   Answer: B

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7. Question 297 · 313 in bank

   A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

   • ACapture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
   • BSet up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
   • CDeploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
   • DUse Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

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   Answer: D

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8. Question 298 · 313 in bank

   Select 2

   A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow. Which solutions will meet these requirements? (Choose two.)

   • AImplement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
   • BUse AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
   • CProvision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined scheduleю
   • DUse Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
   • EBuild an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.

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   Answer: AB

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9. Question 299 · 313 in bank

   A media company uploads large video files to Amazon S3 for processing. After processing, the company needs to keep the original files for 90 days in case the files require reprocessing. After 90 days, the company can delete the files to reduce storage costs. The company stores the processed videos in a different S3 bucket. Which S3 Lifecycle configuration will meet these requirements for the original files MOST cost-effectively?

   • AStore the files in S3 Standard for 90 days. Transition the files to S3 Glacier Flexible Retrieval for long-term storage. Then expire the files.
   • BStore the files in S3 Standard for 90 days. Enable versioning. Enable Object Lock on the files for 90 days. Then expire the files.
   • CStore the files in S3 Standard for 90 days. Implement S3 Lifecycle management to expire the files.
   • DStore the files in S3 Intelligent-Tiering for 90 days. Enable versioning. Add S3 Lifecycle management to expire the files.

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   Answer: C

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10. Question 300 · 313 in bank

    A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks. The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student’s selected language. Which solution will meet these requirements with the LEAST operational overhead?

    • ABuild a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queues the database when generating responses with Amazon Bedrock.
    • BCreate a knowledge base in Amazon Bedrock Knowledge Bases with the university’s textbooks. Configure a multilingual model to generate responses with source citations.
    • CUse Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.
    • DUse Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university’s textbooks to generate personalized feedback with citations.

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    Answer: B

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DEA-C01 practice, question 291. 313 questions available.