DBS-C01 Workload-Specific Database Design Practice Question
A financial services company uses Amazon Redshift for analytics. The workload consists of a mix of short-running queries from dashboards and long-running ETL jobs. The company notices that during peak hours, short queries experience high latency due to queueing behind ETL jobs. How can the company reduce the impact of ETL jobs on dashboard queries?
⚠ Common exam trap
A common mix-up: candidates confuse concurrency scaling or SQA as solutions for queueing, but these features do not isolate workloads; they only add capacity or prioritize within a single queue, whereas WLM queue separation directly addresses the root cause by dedicating resources per workload type.
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
✓
Configure workload management (WLM) queues to separate ETL and dashboard queries, and assign different concurrency levels.
Amazon Redshift's Workload Management (WLM) allows you to create separate queues for different query types, such as ETL jobs and dashboard queries. By assigning different concurrency levels to each queue, you prevent long-running ETL jobs from consuming all available slots and blocking short dashboard queries, thereby reducing latency 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.
- ✓
Configure workload management (WLM) queues to separate ETL and dashboard queries, and assign different concurrency levels.
Why this is correct
WLM allows resource allocation per queue, ensuring dashboard queries have dedicated resources.
- ✗
Enable concurrency scaling to handle bursts of queries.
Why it's wrong here
Concurrency scaling adds capacity but does not prioritize dashboard queries.
- ✗
Enable short query acceleration (SQA) to prioritize queries that run under a certain time threshold.
Why it's wrong here
SQA prioritizes short queries but does not prevent long ETL jobs from consuming resources.
- ✗
Increase the number of nodes in the Redshift cluster.
Why it's wrong here
Adding nodes increases overall capacity but does not guarantee priority for short queries.
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