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 many queries are waiting in the queue, and the WLM query queue wait time is high. The cluster uses automatic WLM. Which action should the engineer take to improve query throughput?
⚠ Common exam trap
The trap here is assuming that automatic WLM allows manual concurrency adjustments, or that adding nodes is the first step for queue wait times.
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 automatically add transient clusters for bursts of queries.
Concurrency scaling automatically adds transient clusters to handle query bursts, reducing queue wait times. It is specifically designed to improve throughput during peak periods. Manually increasing concurrency is not possible with automatic WLM. Adding nodes or changing distribution style does not directly address queue wait times caused by concurrency limits.
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 cluster's distribution style to EVEN to balance data across nodes.
Why it's wrong here
Distribution style affects how data is stored across nodes and can impact query performance, but it does not directly influence WLM queue wait times. Changing distribution style requires table redesign and may not resolve concurrency issues. Queue wait times are primarily a function of WLM configuration and cluster capacity. This action is unrelated to the immediate problem of query queuing.
- ✓
Enable concurrency scaling to automatically add transient clusters for bursts of queries.
Why this is correct
Concurrency scaling in Amazon Redshift automatically adds transient clusters to handle bursts of queries, reducing queue wait times. It is designed for workloads with unpredictable query spikes. When queries queue, concurrency scaling kicks in to provide additional capacity. This directly addresses the high queue wait time during peak hours without manual intervention. It is a feature of both automatic and manual WLM.
- ✗
Increase the number of nodes in the cluster to add more compute resources.
Why it's wrong here
Adding nodes increases compute capacity, which can help with overall performance, but it does not directly address query queue wait times caused by WLM configuration. Automatic WLM manages concurrency and memory allocation based on workload. If queries are waiting due to concurrency limits, adding nodes may not resolve the issue if WLM is not tuned. It is a costly solution that may not target the root cause.
- ✗
Modify the WLM configuration to increase the concurrency level for the queue.
Why it's wrong here
With automatic WLM, concurrency is managed automatically based on the number of queries and resource usage. Manually setting concurrency is not possible in automatic WLM; it is only available in manual WLM. Automatic WLM dynamically adjusts concurrency to optimize throughput and memory. Therefore, this action is not applicable and would not be possible in the current setup.
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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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