ARA-C01 Performance Optimization Practice Question
Which action should an architect take to optimize a query that is experiencing significant 'Remote Disk Spilling' during a join operation on large datasets?
⚠ Common exam trap
Candidates often mistakenly select query acceleration service or altering clustering keys to fix remote disk spilling, missing that only scaling up the warehouse size increases the per-node memory required to resolve the issue.
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 warehouse size.
Remote disk spilling occurs when the active memory of a warehouse node is insufficient to hold the working set of data for an operation like a join or aggregation. By increasing the warehouse size, you provide more memory per node, allowing the query to complete in-memory. This prevents the high-latency I/O operations associated with spilling to remote cloud storage, thereby drastically reducing query execution time.
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 multi-cluster warehouse auto-scaling.
Why it's wrong here
Multi-cluster warehouses scale horizontally to handle concurrent user load, not vertically to provide more memory for a single complex query. Spilling happens due to lack of local RAM for a specific operation, which requires a larger node size to increase available memory headroom for the join processing.
- ✓
Increase the warehouse size.
Why this is correct
Increasing the warehouse size doubles the compute and memory resources per node. This extra memory capacity allows the query processing engine to perform operations like hash joins entirely in memory, eliminating the performance penalty of writing temporary data to remote storage, which is the primary cause of slow performance.
- ✗
Change the table join order in the SQL statement.
Why it's wrong here
While join order can influence performance, it does not address the memory limitation causing remote disk spilling. If the dataset size exceeds memory, even an optimized join order will still result in spills. Memory resource allocation is the binding constraint that necessitates a larger warehouse size for better results.
- ✗
Remove the query from the warehouse and use a serverless task.
Why it's wrong here
Serverless tasks do not inherently provide more memory for join operations than standard warehouses. Moving the workload does not solve the underlying memory deficiency. The bottleneck is the resource footprint of the join, which must be addressed by providing adequate hardware capacity to process the data in-memory.
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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
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