Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

A data engineering team ingests JSON events from 50,000 IoT devices into an Amazon Redshift cluster for near-real-time analytics. New sensor attributes are introduced every few weeks, and analysts must be able to query both existing and future attributes with minimal engineering effort and no downtime for backfilling. Which modeling approach best satisfies these requirements while maintaining good query performance?

  • Define an external table in Redshift Spectrum over the JSON files in Amazon S3 and run a nightly CTAS job to flatten the data into a new table whenever attributes change.

  • Normalize the payload into multiple child tables keyed by event ID so that each new attribute can be inserted into its own table and joined at query time.

  • Create a table that stores the full JSON payload in a SUPER column and expose frequently queried attributes through materialized views that parse the SUPER data with PartiQL.

  • Use ALTER TABLE to add a new VARCHAR column for each attribute as it appears and run an UPDATE statement to backfill existing rows.

AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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