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?
Add deduplication and type-cast steps to a DataBrew recipe, then run a recipe job
Open the dataset in a DataBrew project and inspect the column statistics panel manually
Run a DataBrew data profile job against the dataset
Define a ruleset with pattern and uniqueness rules and run a DataBrew ruleset evaluation job
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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