DEA-C02 Performance Optimization Practice Question
A data engineer is tuning a query that joins two large tables. The query is performing a 'Remote Disk Spilling' operation. Which optimization strategy is most effective to resolve this?
⚠ Common exam trap
Candidates often suggest rewriting the query or adding indexes, but Snowflake does not use traditional indexes; scaling up the warehouse is the standard solution to provide more memory for joins.
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
✓
Scale up the warehouse to a larger size.
Remote disk spilling occurs when the memory allocated to the virtual warehouse is insufficient to hold the intermediate result sets of a join or aggregation. By increasing the size of the warehouse, the memory available per node doubles, allowing larger datasets to be processed entirely in-memory. This significantly reduces latency associated with I/O operations and speeds up complex join operations that frequently cause spilling in standard configurations.
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 multi-cluster warehouse scaling policy to 'Maximized'.
Why it's wrong here
Scaling out with additional clusters adds concurrency capacity but does not increase the memory available to a single query. Each query remains bound by the memory limits of the individual node size, meaning spilling will persist regardless of how many clusters are running concurrently in the warehouse.
- ✗
Enable Result Cache by setting USE_CACHED_RESULT to TRUE.
Why it's wrong here
The result cache is managed automatically by Snowflake and cannot be manually enabled or disabled for specific queries. While it improves performance for identical, repeated queries, it does not assist in processing large, non-cached join operations that are currently constrained by memory limits and disk spilling.
- ✓
Scale up the warehouse to a larger size.
Why this is correct
Increasing the warehouse size provides more memory per node. Since a single query can only execute within the memory limits of the nodes assigned to it, a larger warehouse provides the necessary resources to hold intermediate join states in RAM, effectively eliminating the need to spill to remote storage.
- ✗
Implement a materialized view on the joined columns.
Why it's wrong here
Materialized views are best suited for accelerating simple projections and filters. They do not automatically optimize complex multi-table joins involving large datasets in the way that increasing compute resources does. They would add maintenance overhead without necessarily preventing disk spilling during the underlying query execution process.
Visual reference
About these practice questions
One of 229 original DEA-C02 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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 DEA-C02 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 DEA-C02 exam.