ARA-C01 Performance Optimization Practice Question
A Snowflake architect is optimizing a virtual warehouse that serves a mix of short ad-hoc queries and long-running ETL jobs. Users report that ad-hoc queries sometimes wait in the queue for several minutes during ETL execution. The architect wants to reduce queuing without increasing costs unnecessarily. Which two actions should the architect take? (Choose two.)
⚠ Common exam trap
The trap here is thinking that increasing warehouse size or changing timeout parameters will solve queuing, when queuing is a concurrency issue that requires more clusters or workload isolation.
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 with a minimum of 2 clusters.
To reduce queuing for ad-hoc queries during ETL execution, the architect should either enable multi-cluster warehouse to add capacity dynamically or separate the workloads onto different warehouses. Multi-cluster warehouse with a minimum of 2 clusters ensures additional compute is available for queued queries. Separating ETL and ad-hoc workloads eliminates contention entirely. Both actions directly address the root cause of queuing without unnecessarily increasing costs, as they can be scaled independently.
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 multi-cluster warehouse with a minimum of 2 clusters.
Why this is correct
Enabling multi-cluster warehouse allows Snowflake to automatically add clusters when queries are queued, reducing wait times for ad-hoc queries during peak ETL loads. Setting a minimum of 2 clusters ensures that there is always additional capacity available, so short queries can run concurrently with ETL jobs without waiting. This directly addresses the queuing issue while maintaining performance for both workloads.
- ✗
Increase the size of the existing warehouse to X-Large.
Why it's wrong here
Increasing warehouse size provides more compute power per query, which can speed up individual queries, but it does not increase concurrency. Queuing occurs when the number of concurrent queries exceeds the warehouse's capacity, and a larger warehouse still has a fixed number of slots. While larger warehouses can handle more complex queries faster, they do not allow more simultaneous queries. Therefore, this action may not reduce queuing for many short queries.
- ✓
Create a separate warehouse for ETL jobs and another for ad-hoc queries.
Why this is correct
Isolating workloads by creating separate warehouses for ETL and ad-hoc queries prevents resource contention. ETL jobs can run on a dedicated warehouse without blocking ad-hoc queries, and vice versa. This eliminates queuing for ad-hoc queries because they no longer compete with ETL for the same compute resources. It also allows independent scaling and cost management for each workload, which is a best practice for mixed workloads.
- ✗
Set the warehouse to auto-suspend after 60 seconds of inactivity.
Why it's wrong here
Auto-suspend after 60 seconds helps reduce costs when the warehouse is idle, but it does not address queuing during active periods. In fact, if the warehouse suspends and resumes frequently, it could add latency. The problem is queuing during ETL execution, so the solution must provide more concurrency, not faster suspension. This action might save costs but does not solve the performance issue.
- ✗
Set the STATEMENT_QUEUED_TIMEOUT_IN_SECONDS parameter to a low value.
Why it's wrong here
Setting a low statement queued timeout causes queries to fail if they wait too long in the queue, rather than reducing wait times. This would result in errors for ad-hoc queries during ETL execution, which is undesirable. The goal is to reduce queuing, not to abort queries. This parameter is useful for preventing indefinite waits, but it does not improve performance or concurrency.
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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
This ARA-C01 practice question is part of Courseiva's free Snowflake 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 ARA-C01 exam.