DEA-C01 Data Operations and Support Practice Question
A company runs an Amazon Redshift cluster for analytics. During peak hours, query performance degrades significantly. The data engineer notices that disk space usage is above 80% on many nodes. Which of the following is the MOST effective long-term solution to improve query performance?
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
✓
Resize the cluster to include additional nodes.
Resizing the cluster to include additional nodes increases both storage and compute capacity, directly addressing the high disk usage and improving query performance. Increasing WLM queue slots (Option A) only manages concurrency but does not add capacity. Compression encoding (Option C) reduces storage but may not alleviate immediate performance degradation, and is not a long-term solution for capacity. Running VACUUM (Option D) reclaims space from deleted rows but does not add new capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the workload management (WLM) queue slots.
Why it's wrong here
WLM changes affect concurrency but not disk space.
- ✓
Resize the cluster to include additional nodes.
Why this is correct
Adding nodes increases both storage and compute resources, directly addressing disk usage and performance.
- ✗
Apply compression encoding to all columns.
Why it's wrong here
Compression reduces storage per row but may not be enough to bring usage below 80%.
- ✗
Run the VACUUM command to reclaim space.
Why it's wrong here
VACUUM reclaims space from deleted rows but does not increase total capacity.
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