ARA-C01 Performance Optimization Practice Question
A Snowflake architect is analyzing a query that performs a large join between a fact table and a dimension table. The query profile shows that the join operation is spilling to local disk. The architect wants to reduce the spillage and improve performance. Which action should the architect take first?
⚠ Common exam trap
The trap here is assuming that clustering or query acceleration will fix join spilling, when the primary cause is insufficient memory for the join operation.
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 used for the query.
Spilling to local disk during a join indicates that the operation requires more memory than available. Increasing the virtual warehouse size provides more memory and compute resources, allowing the join to process data in memory and reducing spillage. This is a direct and effective first step. Other options may help in specific cases but do not address the immediate memory constraint.
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 size of the virtual warehouse used for the query.
Why this is correct
Increasing the warehouse size provides more memory and compute resources, which can reduce or eliminate spilling to local disk. Spilling occurs when the operation's working set exceeds available memory. A larger warehouse has more memory per node, allowing the join to process more data in memory. This is often the quickest and most effective first step to address spilling.
- ✗
Enable the Query Acceleration Service on the warehouse.
Why it's wrong here
The Query Acceleration Service offloads portions of eligible queries to shared compute resources, but it is designed for queries with large scans and filters, not for join spilling. It may not help with join operations that spill due to memory constraints. The service targets a specific set of query patterns and does not directly increase memory for joins.
- ✗
Rewrite the query to use a smaller dimension table.
Why it's wrong here
Rewriting the query to use a smaller dimension table is not always possible and does not directly address the spilling issue. If the join is spilling, it is likely due to the size of the data being processed, not the dimension table size alone. Reducing the dimension table might help, but it is not a guaranteed solution and may not be feasible. Increasing warehouse size is a more direct fix.
- ✗
Add a clustering key to the fact table on the join column.
Why it's wrong here
Clustering on the join column can improve pruning and reduce the amount of data scanned, which might indirectly reduce spilling. However, if the join itself is spilling due to insufficient memory for the join operation, clustering alone may not resolve the issue. It is a valid optimization but not the first action to take when spilling is observed. Increasing warehouse size directly addresses memory constraints.
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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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