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?
⚠ Common exam trap
DEA-C01 often tests whether candidates confuse scaling out (adding nodes) with optimizing query and table design, so 'add more nodes' looks appealing but does not reduce CPU utilization per node.
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.
Option A is correct because concurrency scaling automatically adds transient cluster capacity to handle bursts of read (SELECT) queries, offloading that work from the main cluster and thereby reducing its CPU utilization. Option C is correct because choosing appropriate sort keys and compression encodings reduces the amount of data scanned and I/O performed, which directly lowers CPU work during query execution. Option B is not the best fit because adding nodes increases capacity but does not address inefficient queries or design, and it is a costlier, less targeted remedy for CPU pressure. Option D is not correct because VACUUM reclaims space and re-sorts rows after deletes/updates; it is maintenance rather than a primary CPU-reduction action and can itself consume significant resources. Option E is not correct because audit logging only records connection and user activity for monitoring/compliance and does nothing to reduce CPU usage.
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
Concurrency scaling adds transient Redshift clusters that serve read queries, so eligible read workloads move off the main cluster and its CPU utilisation drops. This directly addresses the high CPU constraint by offloading read query processing rather than resizing or tuning the existing cluster.
- ✗
Increase the number of nodes in the cluster.
Why it's wrong here
Adding nodes redistributes data and adds compute, but the stem asks for actions that reduce CPU usage on the existing cluster; scaling out changes capacity rather than lowering the load. It is tempting because elastic resize is the standard remedy for a genuinely undersized cluster running sustained high utilisation.
- ✓
Optimize the table design by using sort keys and compression.
Why this is correct
Sort keys and compression reduce the data scanned and processed per query, cutting CPU work on the cluster. This directly addresses the high CPU utilisation observed in CloudWatch by improving query efficiency through better table design.
- ✗
Run the VACUUM command on all tables.
Why it's wrong here
VACUUM reclaims storage and re-sorts rows, which is I/O-intensive and can itself raise CPU, so it does not reduce utilisation. It is tempting because VACUUM genuinely improves query performance after heavy deletes or updates, but that is a maintenance scenario, not a CPU-reduction one.
- ✗
Enable audit logging to monitor queries.
Why it's wrong here
Audit logging records connection and query activity to S3; it adds overhead and changes nothing about the workload driving CPU. It is tempting because query monitoring is a legitimate diagnostic step when investigating which queries consume the most resources, but it is observability, not remediation.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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