DEA-C01 Data Operations and Support Practice Question
A data engineer is monitoring Amazon CloudWatch metrics for an Amazon Redshift cluster and notices high CPU utilization. The engineer wants to reduce CPU usage. Which TWO actions should the engineer take?
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
✓
Enable concurrency scaling to offload read queries to additional clusters.
Options A and C are correct. Enabling concurrency scaling offloads read queries to additional clusters, reducing CPU load on the main cluster. Optimizing table design with sort keys and compression reduces the amount of data scanned per query, lowering CPU usage. Option B (increasing node count) increases overall CPU capacity but does not reduce usage; it may even increase cost without addressing inefficiency. Option D (running VACUUM) primarily reclaims disk space and maintains data distribution, not CPU reduction. Option E (enabling audit logging) adds CPU overhead, making it counterproductive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable concurrency scaling to offload read queries to additional clusters.
Why this is correct
Offloads queries, reducing CPU on main cluster.
- ✗
Increase the number of nodes in the cluster.
Why it's wrong here
This increases total CPU but does not reduce usage per node.
- ✓
Optimize the table design by using sort keys and compression.
Why this is correct
Reduces data scanned, lowering CPU.
- ✗
Run the VACUUM command on all tables.
Why it's wrong here
VACUUM is for reclaiming space, not CPU reduction.
- ✗
Enable audit logging to monitor queries.
Why it's wrong here
Audit logging adds CPU overhead.
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