AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company is building a data lake on Amazon S3 and wants analysts in multiple AWS accounts to easily discover and query new CSV and Parquet files as soon as they arrive. The solution must automatically capture schema and partition information and store it in a centralized catalog that Lake Formation permissions can secure. Which action should the data engineer take first to create the required data catalog?
Use AWS DataSync to copy the files to an Amazon EMR HDFS cluster and rely on Hive metastore auto-creation during Spark jobs.
Run daily MSCK REPAIR TABLE statements in Amazon Athena to add partitions without using a catalog.
Create an AWS Glue database, then configure a Glue crawler with an IAM role permitted by Lake Formation to crawl the S3 prefixes on a schedule.
Export an Amazon S3 Inventory report to CSV and load the object list into external tables using Amazon Redshift Spectrum.
The AWS Glue Data Catalog is the recommended centralized metadata store for Lake Formation. Creating a Glue database establishes the namespace for the tables that represent each dataset. An AWS Glue crawler pointed at the relevant S3 prefixes inspects the files, infers the schema, detects partitions, and populates or updates the tables in the database. Once tables exist, Lake Formation can manage fine-grained permissions for cross-account access. The other options either rely on manual steps, do not populate the Glue Data Catalog, or do not integrate with Lake Formation security, so they fail to meet the automation and discoverability requirements.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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