AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests daily CSV files into Amazon S3 from several external partners. A data engineer must confirm that the order_date field uses the YYYY-MM-DD format and that the order_id column contains only unique values before the data is loaded into downstream pipelines. The engineer wants an automated, no-code solution that produces a concise report of any violations. Which AWS Glue DataBrew approach should the engineer use?
Define a ruleset with pattern and uniqueness rules and run a DataBrew ruleset evaluation job
Add deduplication and type-cast steps to a DataBrew recipe, then run a recipe job
Run a DataBrew data profile job against the dataset
Open the dataset in a DataBrew project and inspect the column statistics panel manually
AWS Glue DataBrew lets you create data quality rule sets that express consistency requirements such as pattern matching and column uniqueness. Running a ruleset evaluation job automatically tests the dataset against the rules, stores the results in Amazon S3, and presents a summary that highlights any rows that fail. Profile jobs and interactive projects can reveal statistics but do not continuously evaluate explicit rules, while recipe jobs focus on transforming-rather than validating-the data.
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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