A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the…

Question

Options

• ACreate an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a
• BCreate an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS
• CCreate an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive
• DCreate an Amazon SageMaker Jupyter notebook instance that transforms the data. Then, create

• A
  4% (1)
• B
  83% (20)
• C
  4% (1)
• D
  8% (2)

Explanation

Option B is correct because AWS Glue is a fully managed, serverless service purpose-built for exactly this use case - crawlers automatically discover and catalog S3 data into the Glue Data Catalog, and Glue ETL jobs handle transformations without any infrastructure provisioning or ongoing cluster management.

Options A and C are wrong for the same core reason: both require provisioning and maintaining Amazon EMR clusters (with Hive or Spark), along with manually configuring a Hive metastore - this is significantly more setup and ongoing operational burden than the question permits.

Option D is wrong because SageMaker Jupyter notebooks are designed for interactive machine learning workflows, not for automated, repeatable ETL transformation pipelines or metadata cataloging at scale.

Memory tip: When an AWS question emphasizes least setup and maintenance for S3 data transformation + metadata cataloging, think "Glue does it all" - crawl, catalog, transform, serverless. If you see EMR, remember it means you're managing servers; if you see SageMaker notebooks, remember they're for data scientists, not data pipelines.

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