AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests a daily CSV of ecommerce transactions into Amazon S3. The data engineering team must fail the downstream ETL pipeline if more than 1% of rows have a null customer_id or if any order_date is before 2020-01-01. They want to define these checks once and reuse them on future datasets without coding. Which AWS feature should they use?
Invoke an AWS Lambda function from Amazon EventBridge to scan the file and throw an error when invalid rows are found.
Create an AWS Glue DataBrew data quality ruleset and attach it to a DataBrew profile job.
Configure an AWS Glue crawler with custom classifiers to reject files that do not meet the conditions.
Run a scheduled Amazon Athena query that selects rows violating the conditions and check if the result set is empty.
AWS Glue DataBrew provides reusable data quality rulesets that can be attached to profile jobs. Each rule can test conditions such as NOT_NULL or a date range, and a threshold (for example, fewer than 99% valid rows) can mark the ruleset as failed. The job then publishes a CloudWatch event that downstream workflows can use to stop the pipeline. Athena queries, Lambda code, and Glue crawler classifiers do not offer reusable, no-code rules with failure thresholds.
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AWS Certified Data Engineer Associate DEA-C01
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