ARA-C01 Performance Optimization Practice Question
An architect is investigating query performance issues where queries are spilling to local disk. Which TWO actions would most effectively mitigate this issue?
⚠ Common exam trap
Candidates often think increasing the maximum concurrency clusters will solve memory spilling issues, confusing horizontal scalability with node memory capacity.
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
✓
Resize the virtual warehouse to a larger size.
Spilling to disk occurs when the data required for an operation (like a Join or Sort) exceeds the memory available in the warehouse's compute nodes. By scaling up the warehouse, you increase the memory capacity per node. Alternatively, optimizing the query logic to reduce the volume of data being shuffled or sorted ensures that intermediate result sets fit within the available memory heap of the virtual warehouse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Resize the virtual warehouse to a larger size.
Why this is correct
Scaling up a warehouse increases the amount of memory available for operations on each compute node. This directly accommodates larger intermediate datasets that would otherwise be forced to spill to local SSDs during complex join or sort operations, thereby significantly improving query performance for memory-intensive workloads.
- ✗
Increase the warehouse multi-cluster scale factor.
Why it's wrong here
Increasing the multi-cluster scale factor adds more clusters to handle concurrent query demand. It does not provide additional memory to a single, memory-intensive query that is spilling to disk. Therefore, it has no impact on individual query performance related to local memory limitations and disk spills.
- ✓
Optimize the query to reduce the volume of data shuffled.
Why this is correct
Reducing data volume, such as by filtering early or eliminating unnecessary columns, decreases the memory footprint required for sort and join operations. By minimizing the amount of data that needs to be held in memory, you reduce the likelihood of memory exhaustion and subsequent spilling to local disk.
- ✗
Enable query result cache.
Why it's wrong here
The result cache stores the output of previous queries to serve identical subsequent queries instantly. It does not impact the memory capacity of the compute nodes during execution, and therefore it cannot prevent disk spilling for complex, memory-intensive queries that are being executed for the first time.
- ✗
Convert the table to a temporary table.
Why it's wrong here
Temporary tables are local to the session and exist only for the duration of the session. While they manage metadata differently, they do not inherently provide more memory for join or sort operations. Converting to temporary tables does not resolve the memory pressure that leads to disk spilling.
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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 ARA-C01 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 ARA-C01 exam.