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Data Store ManagementhardMultiple ChoiceObjective-mapped

DEA-C01 Data Store Management Practice Question

A company is using Amazon Redshift for analytics. The cluster has 20 nodes and the data is evenly distributed. Query performance has degraded over time. The data engineer suspects that table maintenance is needed. Which set of operations should be performed to improve query performance?

⚠ Common exam trap

The trap here is that candidates familiar with PostgreSQL may mistakenly apply PostgreSQL-specific maintenance commands like VACUUM FULL or REINDEX, not realizing that Amazon Redshift is based on a different architecture (columnar storage, no indexes) and only supports VACUUM and ANALYZE for table maintenance.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Run VACUUM and ANALYZE commands on all tables

Over time, Amazon Redshift tables accumulate deleted rows and unsorted data due to UPDATE and DELETE operations, which degrades query performance. Running VACUUM reclaims space and re-sorts data according to the table's sort key, while ANALYZE updates table statistics used by the query optimizer. Together, these operations restore data layout and enable efficient query planning, directly addressing the performance degradation.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Run VACUUM and ANALYZE commands on all tables

    Why this is correct

    VACUUM reclaims space and sorts rows; ANALYZE updates statistics for the optimizer.

  • Run VACUUM FULL on all tables

    Why it's wrong here

    VACUUM FULL locks tables and is resource-intensive.

  • Run REINDEX on all tables

    Why it's wrong here

    Redshift uses sort keys, not B-tree indexes.

  • Run ALTER TABLE APPEND to reorganize data

    Why it's wrong here

    ALTER TABLE APPEND moves data between tables.

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