AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineer is using AWS Glue DataBrew to explore a new CSV dataset stored in Amazon S3. The analytics team requests an automated report that visually summarizes column-level completeness, unique values, distributions, and correlations so they can quickly spot data-quality issues without writing any SQL. Which DataBrew job type should the engineer configure to deliver this report?
A DataBrew recipe job that runs the project's transformation steps
An Amazon Athena query scheduled with AWS Step Functions
An AWS Glue crawler that catalogs the dataset schema
AWS Glue DataBrew profile jobs compute statistics such as missing values, duplicate counts, correlations, and value-distribution histograms for every column in the selected dataset. When the job finishes, DataBrew writes the raw profile results as JSON to Amazon S3 and renders an interactive profile dashboard in the console that analysts can explore. Recipe jobs, Glue crawlers, and scheduled Athena queries can transform or catalog data, but they do not generate the comprehensive visual data-quality summary requested.
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What is the purpose of an AWS Glue DataBrew profile job?
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How does a DataBrew profile job differ from a recipe job?
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What information is generated by a DataBrew profile job and where is it stored?
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What is an AWS Glue DataBrew profile job?
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How does a DataBrew profile job differ from a recipe job?
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Why can't an AWS Glue crawler or Athena query provide the same insights as a DataBrew profile job?
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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