COF-C03 Practice Question: Snowflake AI Data Cloud Features and Architecture
A Snowflake user executes a complex query that joins a large fact table with several dimension tables. The query takes longer than expected. The user notices that the query plan shows a significant amount of data being spilled to local disk. The warehouse size is currently MEDIUM. What is the most likely cause of the spillage, and what is the recommended action?
⚠ Common exam trap
The trap here is assuming that multi-cluster warehouses or result caching can solve spillage, when the real issue is per-query memory.
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 query is too complex for the MEDIUM warehouse; increasing the warehouse size to LARGE will provide more memory and reduce spillage.
Spillage to local disk indicates that the query's intermediate results exceed the available memory in the warehouse. Increasing the warehouse size provides more memory per node, which can accommodate larger working sets and reduce spillage. This is the recommended action when spillage is observed on a MEDIUM warehouse for a complex query.
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 query is experiencing contention due to multiple concurrent queries; setting the warehouse to multi-cluster will reduce spillage.
Why it's wrong here
Multi-cluster warehouses help with concurrency by adding more clusters, but they do not increase the memory available to a single query. Each query still runs on a single cluster with the same resources. Spillage is caused by insufficient memory for the query's data processing, not by concurrency. Adding clusters may worsen the issue if resources are shared.
- ✓
The query is too complex for the MEDIUM warehouse; increasing the warehouse size to LARGE will provide more memory and reduce spillage.
Why this is correct
Spillage to local disk occurs when the warehouse's memory is insufficient to hold intermediate results. Increasing the warehouse size provides more memory per node, which can reduce or eliminate spillage. The MEDIUM warehouse may be undersized for the query's working set. Scaling up to LARGE is a common remedy for memory-intensive operations like large joins and aggregations.
- ✗
The query is spilling due to a lack of local disk space on the warehouse; increasing the warehouse size will provide more local disk.
Why it's wrong here
Spillage to local disk is a symptom of memory pressure, not disk space shortage. Snowflake warehouses have sufficient local disk for spilling; the issue is that the query's working set exceeds memory. Increasing warehouse size increases memory, which reduces the need to spill. The amount of local disk is not the limiting factor.
- ✗
The query is spilling because the result cache is disabled; enabling the result cache will prevent spillage.
Why it's wrong here
The result cache stores the results of previous queries to avoid re-execution, but it does not affect the memory usage during query execution. Spillage occurs during processing when intermediate results exceed memory. Enabling the result cache would not help if the query has not been run before or if the underlying data has changed. It is not a solution for spillage.
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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.