AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team ingests daily CSV files to an S3 landing bucket. A Step Functions workflow then triggers an AWS Glue ETL job that writes the data to Parquet. The team must first ensure that the order_id and total_amount columns contain no NULL values. If validation fails, the workflow should stop and alert the on-call engineer. With minimal code changes, which solution meets these requirements MOST effectively?
Create an AWS Glue DataBrew profile job with a ruleset that checks for missing values in the two columns, invoke it as the first Step Functions task, and use EventBridge to send an SNS alert if the job status is FAILED.
Add an Amazon Athena task to the workflow that runs a SELECT COUNT(*) query for NULL values in the two columns and branches execution based on the query result.
Enable Amazon Macie on the landing bucket and configure findings filters; stop the workflow and alert the team when Macie generates findings.
Load each file into an Amazon Redshift staging table with NOT NULL constraints on the two columns; proceed only if the COPY command succeeds, otherwise publish an SNS alert.
AWS Glue DataBrew can apply data quality rules to files stored in Amazon S3 without writing code. A DataBrew job that uses a ruleset for "missing value" checks on order_id and total_amount will return a FAILED status when any rule is violated. The job's status is published to CloudWatch, where an EventBridge rule can trigger an SNS notification. By adding this DataBrew job as the first state in the existing Step Functions workflow, the team blocks downstream processing when validation fails. Athena queries or Redshift staging tables require additional SQL and infrastructure, while Amazon Macie is intended for sensitive-data discovery, not schema validation.
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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