ARA-C01 Performance Optimization Practice Question
A data architect is tuning a Snowflake virtual warehouse that runs a mix of short ad-hoc queries and long-running analytical queries. The warehouse is sized as Medium, and the architect observes that short queries are frequently queued behind long queries. The architect wants to reduce queuing for short queries without increasing the warehouse size. Which configuration change should the architect make?
⚠ Common exam trap
The trap here is thinking that multi-cluster warehouses solve all concurrency issues, but they add clusters for overall load and do not prioritize short queries over long ones.
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 warehouse for short ad-hoc queries and route them accordingly.
Workload isolation by creating a separate warehouse for short ad-hoc queries prevents them from queuing behind long analytical queries. This allows independent sizing and scaling, improving performance for both workloads without increasing the size of the original warehouse. It is a standard Snowflake best practice for managing mixed workloads and reducing contention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a separate warehouse for short ad-hoc queries and route them accordingly.
Why this is correct
Separating short ad-hoc queries onto a dedicated warehouse isolates them from long-running analytical queries, preventing queuing behind long queries. This approach allows each warehouse to be sized and scaled independently, optimizing for the specific workload. It is a common best practice for workload isolation and improves concurrency without increasing the size of the original warehouse.
- ✗
Enable the Query Acceleration Service on the warehouse.
Why it's wrong here
The Query Acceleration Service offloads portions of eligible queries to shared compute resources, which can improve performance for large scans, but it does not address queuing for short queries. It targets long-running queries with large scans, not concurrency issues. Enabling it would not reduce the queuing of short ad-hoc queries behind long analytical queries.
- ✗
Configure the warehouse with a multi-cluster scaling policy set to Standard.
Why it's wrong here
A multi-cluster warehouse with Standard scaling adds clusters when queries are queued, which can reduce queuing. However, it increases cost and may not be the most efficient solution when the goal is to prioritize short queries. The Standard scaling policy starts clusters based on load, but it does not differentiate between short and long queries. It is a valid option for concurrency, but not the best fit here.
- ✗
Set the warehouse to auto-suspend after 60 seconds and auto-resume when queued.
Why it's wrong here
Auto-suspend and auto-resume settings control warehouse idling and cost, not query prioritization. They do not prevent short queries from queuing behind long queries while the warehouse is running. Adjusting these settings may affect cost but will not resolve the concurrency issue described. Workload isolation is the appropriate solution.
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JA
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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