DEA-C01 Data Store Management Practice Question
A company has an Amazon Redshift cluster that stores petabytes of data. Queries are experiencing high disk usage due to large intermediate results. The data engineer needs to improve query performance without adding more nodes. Which action should the engineer take?
⚠ Common exam trap
The trap here is conflating storage/scan optimizations (compression, sort keys) with join-time data movement; candidates pick compression or sort keys because they sound like general performance fixes, but only distribution keys address the intermediate-result disk spill caused by row redistribution.
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
✓
Set appropriate distribution keys to minimize data movement.
In Amazon Redshift, distribution keys determine how table rows are spread across compute nodes. When joined tables share the same distribution key (or use ALL distribution for small dimension tables), matching rows are co-located on the same node slice, eliminating the broadcast or redistribution steps that spill large intermediate result sets to disk. Since the question specifies high disk usage from large intermediate results and prohibits adding nodes, fixing distribution keys directly reduces the data movement that generates those spills.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set appropriate distribution keys to minimize data movement.
Why this is correct
Distribution keys control which node stores each row, so choosing them well colocates joined rows and reduces data redistribution across nodes. That cuts the disk spill from large intermediate results without adding nodes, directly addressing the stated constraint.
- ✗
Configure workload management (WLM) queues to limit concurrency.
Why it's wrong here
WLM queue concurrency limits govern how many queries run simultaneously; they do not reduce the disk consumed by large intermediate results. It is tempting because WLM is the correct tool when the goal is to prevent one workload from monopolising cluster resources.
- ✗
Apply column compression encoding to reduce data size.
Why it's wrong here
Column compression encoding reduces stored table size and I/O, yet it does not address the disk consumed by large intermediate results generated during query execution. It is tempting because compression is the correct choice when storage footprint or scan volume, rather than intermediate spilling, is the bottleneck.
- ✗
Define sort keys on all columns used in WHERE clauses.
Why it's wrong here
Sort keys accelerate range-restricted scans by enabling zone-map block skipping, but they do not shrink the intermediate result sets spilling to disk. It is tempting because sort keys are the correct choice when queries filter heavily on a specific column and scan cost dominates.
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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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