ARA-C01 Snowflake Architecture Practice Question
An architect is designing a multi-cluster warehouse for a retail analytics workload that experiences unpredictable spikes in concurrency. The warehouse is configured with a minimum of 2 clusters and a maximum of 10 clusters, scaling policy set to Economy. During a sudden spike, queries are queuing despite the warehouse scaling out to 6 clusters. What is the most likely cause of the queuing?
⚠ Common exam trap
The trap here is assuming that queuing always means the maximum cluster count is too low, when the scaling policy's aggressiveness can also cause temporary queuing.
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
✓
The Economy scaling policy adds clusters more slowly than the Standard policy, causing queries to queue during rapid spikes.
The Economy scaling policy prioritizes cost by scaling out slowly and scaling in quickly. During a sudden spike, it may not add clusters fast enough to prevent queuing, even if the maximum cluster count is not reached. The Standard policy would scale out more aggressively to minimize queuing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The Economy scaling policy adds clusters more slowly than the Standard policy, causing queries to queue during rapid spikes.
Why this is correct
The Economy scaling policy is designed to favor cost savings by scaling out more conservatively and scaling in aggressively. It may take longer to add clusters during a sudden spike, leading to temporary queuing even if the maximum cluster count has not been reached. The Standard policy would add clusters more quickly, reducing queuing at the expense of higher cost.
- ✗
The queries are not suitable for multi-cluster warehouses because they are too complex.
Why it's wrong here
Multi-cluster warehouses are designed to handle concurrency for any query type. Query complexity affects execution time but does not prevent scaling. The issue is the scaling policy's behavior during rapid spikes, not the nature of the queries.
- ✗
The warehouse is configured with a maximum cluster count that is too low for the workload.
Why it's wrong here
The maximum cluster count is 10, and the warehouse scaled to only 6 clusters, so the maximum is not the limiting factor. If it were, the warehouse would attempt to scale to 10 and still queue. The queuing at 6 clusters suggests another bottleneck, such as the scaling policy or resource contention.
- ✗
The warehouse has reached the limit of concurrent queries per cluster, so adding more clusters does not help.
Why it's wrong here
Snowflake does not have a fixed limit on concurrent queries per cluster; it queues queries when resources are exhausted. Adding more clusters increases total resources. The queuing is due to the scaling policy's delay in adding clusters, not an inherent per-cluster concurrency limit.
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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
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.