COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A data engineer observes that a transformation query run on a MEDIUM warehouse spends most of its time in the 'Remote Disk Spilling' phase of the Query Profile. The query joins two large tables and performs a large sort. Memory usage shows the warehouse consistently near its limit. The engineer wants the most direct fix that addresses the root cause. Which action should be taken?
⚠ Common exam trap
The trap here is treating remote spilling as a scan-efficiency problem and reaching for clustering or query acceleration, when the spill happens in memory-intensive join and sort operators.
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
✓
Increase the size of the virtual warehouse so more memory is available per node and spilling is avoided.
Remote disk spilling indicates that the join and sort working set exceeded warehouse memory and intermediate data was written to remote storage. The most direct remedy is to give the operation more memory by scaling up the virtual warehouse, which increases the memory available per node. Query rewriting, query acceleration, and clustering all fail to address the memory shortfall that the Query Profile is reporting.
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 query acceleration service on the warehouse to offload the sort to shared compute.
Why it's wrong here
The query acceleration service targets scans with selective filters by offloading portions of table scanning to shared compute. It does not add memory for join and sort operators, which is where the spilling occurs. Enabling it would not relieve the memory pressure that causes the remote spill.
- ✗
Rewrite the query to use a smaller data type for the join keys so less memory is consumed per row.
Why it's wrong here
Narrower data types can reduce memory per row, but the spilling is driven by the sheer number of rows being sorted and joined, not by the width of a single key. The warehouse is already near its memory limit, so a marginal reduction will not eliminate the spill. This is a micro-optimization that does not address the root cause.
- ✓
Increase the size of the virtual warehouse so more memory is available per node and spilling is avoided.
Why this is correct
Remote disk spilling means the operation exceeded the memory available on its warehouse and had to write intermediate data to remote storage, which is far slower. Scaling up the warehouse adds memory capacity to handle the join and sort working set. This directly addresses the memory shortfall that the Query Profile is showing.
- ✗
Add a clustering key to the larger table so fewer micro-partitions are scanned during the join.
Why it's wrong here
Clustering reduces the number of micro-partitions scanned when a selective filter is present, but this query joins two large tables and sorts a large result. The spill happens after the scan, in the join and sort operators, so reducing scanned partitions does not lower the working set size. It also does not add memory.
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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.