Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

A company keeps 5 years of point-of-sale data as partitioned Parquet files in an S3 data lake. Business analysts want to run SQL queries from Amazon Redshift and expect to add or remove columns in the source dataset several times a month. The data engineering team must avoid rewriting historical files or re-loading large Redshift tables each time the schema changes while still benefiting from columnar storage and compression. Which approach meets these requirements with the least operational effort?

  • Create an external schema in Amazon Redshift Spectrum that points to the Parquet files. Use an AWS Glue crawler to update the external table when columns change.

  • Convert the Parquet data to CSV and ingest it into Amazon Aurora PostgreSQL; apply ALTER TABLE statements when columns are added or removed.

  • COPY the Parquet data into a Redshift managed table with ENCODE AUTO and run ALTER TABLE ADD or DROP COLUMN whenever the schema evolves.

  • Load each day's Parquet files into a DynamoDB table that stores every record as a JSON document so new attributes can be added on demand.

AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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