AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineer needs to archive rows older than 2 years from an Amazon Redshift fact table to Amazon S3. The archived data must remain queryable in Amazon Athena while keeping S3 storage cost as low as possible and completing the export quickly. Which approach satisfies these requirements?
Create an external schema in Redshift Spectrum that points to the existing table without moving any data.
Use a COPY command with FORMAT AS PARQUET to move the selected rows from Redshift to an S3 bucket.
Run an UNLOAD statement that writes the selected rows to Amazon S3 as comma-separated values (CSV) compressed with GZIP.
Run an UNLOAD statement that writes the selected rows to Amazon S3 in PARQUET format and partitions the files by date columns (for example, year and month).
Using Redshift UNLOAD with the PARQUET format writes the data in a columnar layout that Athena can read directly. Parquet files are automatically compressed with Snappy, reducing storage space and scan cost. Adding a PARTITION BY clause creates Hive-compatible folder partitions (for example by year and month), which allows Athena to prune data during queries and improves performance. UNLOAD runs in parallel across Redshift compute nodes, so the operation completes faster than single-threaded alternatives. Exporting CSV files-whether compressed or not-stores more bytes and requires Athena to read every row. COPY is used for loading data into Redshift, not exporting it. Creating an external table without moving the data does not satisfy the archival requirement.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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