DEA-C01 Data Operations and Support Practice Question
A data engineer is managing an Amazon Redshift cluster that experiences performance degradation during peak query hours. The engineer notices that some queries are waiting in the queue for a long time, and the WLM (Workload Management) configuration is set to auto. The engineer wants to implement manual WLM to improve query throughput and ensure that short-running queries are not blocked by long-running ones. Which TWO actions should the engineer take to achieve this? (Choose two.)
⚠ Common exam trap
Test-takers frequently confuse concurrency scaling or QMR as solutions for queue wait times, when they do not provide the necessary workload isolation and resource management.
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 the WLM timeout for the long-running query queue to automatically terminate queries that exceed a specified time.
Manual WLM with separate queues for short and long queries, along with WLM timeouts for long queries, directly addresses the issue of short queries being blocked. Separate queues provide resource isolation, while timeouts prevent long queries from consuming resources indefinitely. These two actions together improve throughput and ensure responsiveness during peak hours.
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 the WLM timeout for the long-running query queue to automatically terminate queries that exceed a specified time.
Why this is correct
Setting a WLM timeout on the long-running queue prevents those queries from monopolizing resources indefinitely. When a query exceeds the timeout, it is terminated, freeing up resources for other queries. This helps maintain throughput and ensures that short queries are not starved. It is a key configuration in manual WLM to manage runaway queries.
- ✓
Create separate WLM queues for short-running and long-running queries, and assign appropriate memory percentages to each queue.
Why this is correct
Creating separate WLM queues allows the engineer to isolate short queries from long ones, preventing head-of-line blocking. By allocating memory percentages, the engineer ensures that each queue has dedicated resources, so short queries can run concurrently without waiting for long queries to finish. This directly addresses the performance degradation during peak hours.
- ✗
Use query monitoring rules (QMR) to log queries that exceed resource thresholds and send alerts.
Why it's wrong here
QMR is useful for monitoring and logging, but it does not actively manage query queues or prevent short queries from being blocked. It provides visibility into performance issues but does not enforce resource allocation or timeouts. The engineer needs to configure WLM queues and timeouts to achieve the desired throughput improvement.
- ✗
Increase the number of nodes in the Redshift cluster to provide more memory and CPU resources.
Why it's wrong here
Adding nodes increases overall cluster capacity, which can improve performance, but it does not address the queue wait times caused by uneven workload distribution. Without proper WLM configuration, long queries can still block short ones. Scaling is a complementary action but not a direct solution for the isolation requirement.
- ✗
Enable concurrency scaling to automatically add cluster capacity for bursts of queries.
Why it's wrong here
Concurrency scaling adds transient capacity to handle bursts, but it does not address the queue wait times caused by long-running queries blocking short ones. It can help with overall throughput but does not provide the isolation needed to prioritize short queries. Manual WLM with separate queues is more effective for ensuring short queries are not blocked.
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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
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.