COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A finance analyst runs a monthly report that aggregates 18 months of sales data. The report executes 40 times per day, and each run currently takes 4 minutes on a medium warehouse. The underlying tables are loaded once nightly. Which approach most effectively reduces compute cost for this workload?
⚠ Common exam trap
The trap here is treating a speed problem as the issue, when the workload is actually a redundancy problem that result caching solves for free.
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 the result cache by ensuring the query text and session context are identical across runs.
The tables are static during the business day, so the 40 daily executions read identical data. Ensuring identical query text and session context lets Snowflake serve subsequent runs from the result cache without provisioning compute, eliminating nearly all of the recurring cost. Resizing, auto-suspend tuning, and multi-cluster scaling change resource behavior but do not remove the redundant scans.
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 result cache by ensuring the query text and session context are identical across runs.
Why this is correct
Because the tables change only nightly, identical query text run repeatedly during the day can be served from the result cache at no compute cost after the first execution. Keeping the SQL text and relevant session parameters consistent allows cache reuse, cutting 39 of 40 daily executions to near-zero credits. This is the most direct cost reduction for a repetitive, read-only report.
- ✗
Set the warehouse to auto-suspend after 60 seconds to avoid idle credits.
Why it's wrong here
Auto-suspend reduces credits during idle periods, which helps when the warehouse sits unused. However, the report runs 40 times daily, so the warehouse is active frequently and each wake-up incurs a minimum billing period. This setting trims idle waste but does not eliminate the repeated scan cost that dominates this workload, so it is a partial measure at best.
- ✗
Create a separate virtual warehouse for the analyst and enable multi-cluster scaling.
Why it's wrong here
Multi-cluster warehouses add compute capacity to handle concurrency, which increases credit consumption rather than reducing it. A dedicated warehouse isolates the analyst's workload but does not make the repeated aggregation cheaper. Since the bottleneck is redundant scanning of unchanged data, adding clusters compounds cost instead of addressing the root inefficiency.
- ✗
Resize the warehouse to 4X-Large so each run finishes faster.
Why it's wrong here
A larger warehouse completes the query faster but consumes credits at a proportionally higher rate, so total cost for the same work is roughly unchanged and can even rise due to minimum billing. Since the data is static during the day, speed is not the bottleneck; repeated full scans are. Resizing addresses latency, not the redundant compute the scenario targets.
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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 COF-C03 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 COF-C03 exam.