DEA-C01 Data Store Management Practice Question
A data engineer is managing an Amazon Redshift cluster that experiences performance degradation during peak query loads. The cluster uses a dc2.large node type with 2 nodes. Analysis shows that the workload involves frequent large table scans and complex joins. The engineer wants to improve query performance without changing the overall data volume. Which action should the engineer take?
⚠ Common exam trap
The trap here is assuming that adding more nodes or enabling concurrency scaling will automatically fix performance issues, when the root cause is suboptimal data distribution and sorting.
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
✓
Change the distribution style of large tables to KEY on the join columns and enable sort keys on frequently filtered columns.
Proper distribution and sort keys are fundamental Redshift tuning techniques. Distributing large tables on join keys collocates data, minimizing network traffic during joins. Sort keys on filter columns allow the query engine to skip blocks. These changes directly address the performance bottlenecks without adding hardware, making them the most effective and cost-efficient solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the distribution style of large tables to KEY on the join columns and enable sort keys on frequently filtered columns.
Why this is correct
Choosing an appropriate distribution key ensures that join data is collocated on the same nodes, reducing data movement during joins. Sort keys on filtered columns enable efficient range scans. Together, these optimizations significantly improve performance for large scans and complex joins on a small cluster.
- ✗
Increase the number of nodes in the cluster by resizing to a larger node type with more memory.
Why it's wrong here
Resizing to a larger node type increases cost and may provide more memory, but without proper distribution and sort keys, the underlying data movement and scan inefficiencies remain. It is a costly solution that does not directly address the root cause of slow joins and scans.
- ✗
Enable concurrency scaling to automatically add transient clusters during peak loads.
Why it's wrong here
Concurrency scaling helps with high concurrency by adding transient clusters for read queries, but it does not address performance issues caused by large table scans and complex joins on a small cluster. It adds cost and may not improve individual query performance for resource-intensive operations.
- ✗
Enable short query acceleration (SQA) to prioritize short-running queries.
Why it's wrong here
SQA is designed to improve performance for short queries by isolating them from long-running queries. It does not optimize large table scans or complex joins, which are the primary issues here. It may help with mixed workloads but not with the heavy analytical queries described.
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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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