ARA-C01 Performance Optimization Practice Question
A SnowPro Advanced Architect is configuring a virtual warehouse for a workload that runs a mix of short ad-hoc queries and long-running ETL jobs. The architect wants to prevent long-running ETL jobs from monopolizing the warehouse and degrading ad-hoc query performance. Which approach is most appropriate?
⚠ Common exam trap
The trap here is thinking that multi-cluster warehouses or query acceleration can solve workload contention, when the real solution is to separate workloads onto different warehouses.
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 ETL jobs and use resource monitors to control credit usage.
Workload isolation is best achieved by dedicating separate virtual warehouses to different workload types. ETL jobs and ad-hoc queries have different resource profiles and SLAs; putting them on the same warehouse causes contention. A separate ETL warehouse with its own resource monitor allows the architect to control costs and prevent ETL from affecting ad-hoc performance. Other options either do not isolate workloads or introduce disruptive side effects.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the warehouse to use multi-cluster mode with a minimum of two clusters.
Why it's wrong here
Multi-cluster warehouses help with concurrency by adding clusters when queries queue, but they do not isolate long-running ETL from short ad-hoc queries. Both workloads would still compete for the same warehouse resources, and the ETL jobs could still consume capacity. Multi-cluster is designed for scaling concurrency, not for separating different workload types with different performance characteristics.
- ✗
Enable the Query Acceleration Service on the warehouse to offload portions of the ETL jobs.
Why it's wrong here
Query Acceleration Service can offload parts of eligible queries to shared compute, but it is not designed to isolate workloads or prevent resource contention between ETL and ad-hoc queries. It may help some long-running scans, but it does not provide the workload isolation the architect needs. It also has eligibility criteria and may not apply to all ETL operations, so it is not a reliable isolation mechanism.
- ✗
Set the STATEMENT_TIMEOUT_IN_SECONDS parameter to a low value to terminate long ETL queries.
Why it's wrong here
Setting a low statement timeout would abort long-running ETL queries, which may be undesirable and could cause data pipeline failures. It does not isolate workloads; it simply kills queries that exceed the limit. This approach is disruptive and does not prevent contention while ETL queries are running within the timeout. It is a blunt control rather than a workload isolation strategy.
- ✓
Create a separate warehouse for ETL jobs and use resource monitors to control credit usage.
Why this is correct
Separating ETL and ad-hoc workloads onto different warehouses isolates compute resources, preventing ETL jobs from impacting ad-hoc query performance. Resource monitors can then be attached to the ETL warehouse to cap credit consumption and alert on usage. This is a standard Snowflake best practice for workload isolation and cost control, directly addressing the contention issue without changing query logic.
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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
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