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AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company ingests daily order CSV files into Amazon S3. A Python AWS Glue job converts them to Parquet and loads Amazon Redshift. The team must ensure at least 98% of rows contain a non-null customer_email value and block the load if the threshold fails, while adding minimal new code within the Glue workflow. Which solution meets these requirements?

  • Run an AWS Glue DataBrew profile job after each file arrives, send the profile results to CloudWatch, and use a CloudWatch alarm to invoke an AWS Lambda function that stops the Glue job when the error rate exceeds 2%.

  • Schedule an Amazon Athena query with Amazon EventBridge to count rows with null customer_email values and publish an SNS alert so an operator can cancel the Glue workflow when necessary.

  • Invoke an Amazon Deequ validation script on an Amazon EMR cluster via AWS Step Functions before the Glue job; run the Glue workflow only if the script succeeds.

  • Add an AWS Glue Data Quality transform node to the existing job, define a ruleset that enforces 98% completeness on customer_email, and configure the job to fail when the rule is violated.

AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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