ARA-C01 Performance Optimization Practice Question
A financial services company runs a Snowflake workload where several long-running analytical queries compete with a high volume of short interactive dashboard queries on the same multi-cluster warehouse. Users report that dashboards sometimes wait behind heavy reports. The architect wants a cost-effective configuration that isolates the heavy reports from the interactive queries without duplicating compute unnecessarily. What should the architect do?
⚠ Common exam trap
The trap here is assuming that scaling up a single warehouse or enabling acceleration features will isolate workloads, when only separate warehouses truly prevent one workload from blocking another.
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 the heavy reports and route those queries to it, leaving the dashboard warehouse for interactive queries.
Separate warehouses are the standard way to isolate workloads in Snowflake. By giving heavy reports their own warehouse, the interactive dashboards no longer queue behind them, and each workload can be sized and scaled independently. This avoids over-provisioning a single warehouse and keeps credit usage aligned with each workload's needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the warehouse size to the next larger size so that all queries get more compute resources.
Why it's wrong here
Scaling up a warehouse increases compute per node, but it does not separate long analytical queries from short dashboard queries. Both workloads still share the same queue and resources, so the dashboards can continue to wait behind heavy reports. It also raises credit consumption for all queries, not just the heavy ones, and does not provide workload isolation.
- ✓
Create a separate warehouse for the heavy reports and route those queries to it, leaving the dashboard warehouse for interactive queries.
Why this is correct
Using separate warehouses for the heavy reports and the interactive dashboards isolates the two workloads so they no longer compete for the same compute resources. The dashboard queries run on their own warehouse without waiting behind long reports, and each warehouse can be sized and scaled independently, which is the recommended way to handle mixed workloads cost-effectively.
- ✗
Enable the Query Acceleration Service on the existing warehouse to offload portions of the heavy queries.
Why it's wrong here
The Query Acceleration Service can offload eligible parts of a query to shared compute, but it does not isolate workloads from each other or prevent short dashboard queries from queuing behind long reports. It is designed to accelerate specific query patterns, not to provide workload separation, so it would not solve the contention described.
- ✗
Set the STATEMENT_TIMEOUT_IN_SECONDS parameter to a low value on the warehouse so long queries are cancelled.
Why it's wrong here
A low statement timeout would cancel long-running reports rather than isolate them, which breaks the reporting workload instead of improving dashboard responsiveness. It also does not separate compute resources, so interactive queries would still share the warehouse. This is a destructive workaround, not a workload isolation strategy, and would cause business impact.
About these practice questions
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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.