ARA-C01 Performance Optimization Practice Question
A SnowPro Advanced Architect is tuning a virtual warehouse that experiences high concurrency during peak hours. The architect observes that queries are queuing, and the warehouse is not fully utilizing its resources. Which TWO actions should the architect take to improve concurrency? (Choose two.)
⚠ Common exam trap
It's easy for candidates to confuse scaling up with scaling out, or assuming that Query Acceleration Service can resolve queuing, when concurrency is best solved by adding clusters or isolating workloads.
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 multi-cluster warehouse mode and set a minimum and maximum cluster count.
High concurrency with queuing is best addressed by scaling out compute resources. Multi-cluster warehouses automatically add clusters to handle queuing, and setting min/max clusters controls the scale. Separating workloads onto different warehouses distributes load and reduces contention. Scaling up, auto-suspend, and Query Acceleration Service do not directly improve concurrency; they address different problems such as per-query performance, cost control, or specific query offloading.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the Query Acceleration Service on the warehouse to handle queued queries.
Why it's wrong here
Query Acceleration Service offloads portions of eligible queries to shared compute, but it is not designed to handle query queuing or increase concurrency. It targets specific query patterns, such as large scans with selective filters, and does not add general-purpose concurrency capacity. It would not resolve the queuing issue described. The architect should focus on scaling out or isolating workloads instead.
- ✓
Enable multi-cluster warehouse mode and set a minimum and maximum cluster count.
Why this is correct
Multi-cluster warehouses automatically add clusters when queries queue, allowing the warehouse to scale out for concurrency. Setting a minimum and maximum cluster count controls the scaling range and cost. This directly addresses queuing by providing more compute resources during peak periods. It is the standard Snowflake feature for handling high concurrency without manual intervention, and it can scale back down when demand subsides.
- ✗
Increase the warehouse size to add more compute nodes per cluster.
Why it's wrong here
Scaling up adds more compute nodes per cluster, which can improve performance of individual queries but does not increase the number of concurrent queries a warehouse can handle. In fact, a larger warehouse may consume more credits without addressing queuing. For concurrency, scaling out with multi-cluster is the appropriate approach. Scaling up is better for complex, long-running queries that need more resources per query.
- ✓
Use a separate warehouse for different user groups to distribute the load.
Why this is correct
Creating separate warehouses for different user groups distributes the query load across multiple compute clusters, reducing contention on any single warehouse. This is a form of workload isolation that can improve concurrency for each group. It also allows independent scaling and cost tracking. While it may increase overall credit usage, it is an effective way to handle high concurrency when a single warehouse cannot keep up.
- ✗
Set the warehouse to auto-suspend after a short period to free up resources for other warehouses.
Why it's wrong here
Auto-suspend helps reduce cost when the warehouse is idle, but it does not improve concurrency during peak hours. In fact, if the warehouse suspends during a lull and then must resume, it can add latency. The scenario describes high concurrency with queuing, so the focus should be on adding capacity, not suspending. Auto-suspend is a cost-control feature, not a concurrency feature.
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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 Snowflake exam blueprint
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