MLS-C01 Data Engineering Practice Question
A company is using Amazon Redshift for data warehousing. The data engineering team notices that queries are slow and the system is frequently writing to disk due to insufficient memory. Which type of workload management (WLM) configuration change would help reduce disk writes?
⚠ Common exam trap
A common mix-up: candidates confuse increasing concurrency (Option A) with improving performance, not realizing that higher concurrency reduces per-query memory and increases disk spills, making the problem worse.
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
✓
Increase the memory percentage allocated to the WLM queue.
When queries spill to disk in Amazon Redshift, it indicates that the memory allocated to the WLM queue is insufficient for the workload. Increasing the memory percentage for the queue allows more queries to be processed in memory, reducing the need to write intermediate results to disk and improving query performance.
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 number of query concurrency slots.
Why it's wrong here
More concurrency reduces memory per query, worsening spills.
- ✓
Increase the memory percentage allocated to the WLM queue.
Why this is correct
More memory per query reduces disk spill.
- ✗
Enable query monitoring rules to abort queries that spill to disk.
Why it's wrong here
Aborting queries doesn't fix the underlying memory issue.
- ✗
Enable short query acceleration (SQA).
Why it's wrong here
SQA prioritizes short queries, not memory allocation.
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