DEA-C01 Data Store Management Practice Question
A data engineer manages an Amazon Redshift cluster that runs a nightly ETL load followed by complex analytical queries. Users report that queries during the day are slower than expected, and the team wants to isolate the ETL workload so it cannot consume resources needed by the analytical queries. The cluster uses provisioned nodes. What is the MOST appropriate solution?
⚠ Common exam trap
The trap here is assuming that adding nodes or enabling concurrency scaling will isolate workloads, when isolation requires separate WLM queues with defined memory and concurrency.
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
✓
Create a separate Redshift workload management (WLM) queue for the ETL role and assign the analytical queries to a different queue.
Redshift WLM provides workload isolation by letting you define multiple queues, each with its own memory allocation and query concurrency, and by routing queries to queues based on user groups or query groups. Placing the ETL job in a dedicated queue ensures it cannot consume the memory and slots reserved for analytical queries, which addresses the slowdowns without adding clusters or nodes.
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 nodes in the cluster to provide more resources for all workloads.
Why it's wrong here
Adding nodes increases overall capacity but does not prevent the ETL workload from consuming resources that analytics needs. Without queue separation, both workloads still compete in the same WLM configuration. This scales cost without solving the isolation problem, and it is not the most appropriate fix.
- ✗
Enable concurrency scaling on the cluster and route all queries through it.
Why it's wrong here
Concurrency scaling adds transient clusters to handle bursts of concurrent queries, but it does not isolate a specific workload's resource consumption from others. It is designed for many simultaneous read queries, not for separating ETL from analytics. It also incurs additional cost and does not provide the workload isolation required.
- ✓
Create a separate Redshift workload management (WLM) queue for the ETL role and assign the analytical queries to a different queue.
Why this is correct
Redshift WLM lets you define multiple queues with dedicated memory and concurrency slots, and you can route queries to queues based on user groups or query groups. Assigning ETL to its own queue prevents it from starving the analytics queue. This directly isolates the workloads on the same provisioned cluster without extra infrastructure.
- ✗
Move the ETL workload to a separate Redshift cluster and use Amazon Redshift Spectrum for analytics.
Why it's wrong here
A separate cluster isolates resources but doubles cluster management and cost, and Spectrum queries external data in S3 rather than the local tables used by analytics. This is heavier than needed. The requirement is to isolate workloads on the existing provisioned cluster, which WLM handles without additional clusters.
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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.