ARA-C01 Performance Optimization Practice Question
A Snowflake architect is tasked with improving the performance of a dashboard that runs several queries against a large fact table. The queries filter on different columns and are run concurrently by many users. The architect notices that the warehouse is often queued due to high concurrency. Which configuration change is most appropriate to reduce queuing and improve concurrency?
⚠ Common exam trap
A common mix-up: candidates confuse scaling up (larger warehouse size) with scaling out (multi-cluster), where only scaling out increases concurrent query capacity.
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 and maximum cluster count greater than 1.
High concurrency causing queuing is best resolved by scaling out with a multi-cluster warehouse. By configuring minimum and maximum clusters greater than 1, Snowflake can automatically add clusters when queries are queued, allowing more concurrent queries to run. Scaling up or enabling other features does not increase concurrency capacity.
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.
Why it's wrong here
Query Acceleration Service helps with individual query performance by offloading parts of scans, but it does not address concurrency limits or queuing. It is designed for queries that are I/O-bound, not for scaling out to handle more simultaneous users. Multi-cluster warehouses are the correct feature for concurrency.
- ✓
Enable multi-cluster warehouse with a minimum and maximum cluster count greater than 1.
Why this is correct
Multi-cluster warehouses automatically add or remove clusters based on concurrency and queueing. Setting minimum and maximum clusters greater than 1 allows the warehouse to scale out to handle concurrent queries, reducing queuing. This directly addresses the high concurrency issue described.
- ✗
Increase the warehouse size to a larger T-shirt size.
Why it's wrong here
Scaling up a warehouse adds more compute resources per cluster, which can speed up individual queries but does not increase the number of concurrent queries that can run simultaneously. For high concurrency, scaling out with multi-cluster warehouses is more effective. Scaling up may also increase cost without solving queuing.
- ✗
Set the warehouse to auto-suspend after 60 seconds of inactivity.
Why it's wrong here
Auto-suspend reduces cost by shutting down the warehouse when idle, but it does not improve concurrency or reduce queuing during active periods. In fact, frequent suspends and resumes can add latency. The goal is to handle concurrent queries, so this setting is irrelevant to the problem.
Visual reference
About these practice questions
This ARA-C01 question is part of Courseiva's 209-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.