Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company lands daily CSV order files in Amazon S3. An AWS Glue Spark job loads the data into Amazon Redshift. Each record must contain a non-null customer_id and an order_total greater than 0. If more than 0.5 % of rows break either rule, the pipeline must halt and alert operations; otherwise loading continues. What is the most efficient way to add this validation with minimal new code?

  • Insert an AWS Glue Data Quality transform with a ruleset that stops the job when more than 0.5% of rows fail completeness and custom checks.

  • Create CHECK constraints on the Redshift target table so the COPY command rejects any rows with null customer_id or non-positive order_total.

  • Run an AWS Step Functions workflow that executes an Athena query after loading to count invalid rows and rolls back the transaction if the 0.5% limit is exceeded.

  • Add a DataBrew profile job to scan the CSV files before every Glue run and trigger the Glue job only if the profile shows fewer than 0.5% invalid rows.

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