ARA-C01 Performance Optimization Practice Question
A Snowflake architect is optimizing a slow query that performs a large aggregation over a fact table. The query profile shows that the Aggregation operator is taking a long time. Which TWO actions are most likely to improve the performance of this aggregation? (Choose two.)
⚠ Common exam trap
The trap here is assuming that any performance feature like Search Optimization Service or simply scaling up will fix aggregation slowness, when the key is to optimize how data is grouped and pre-aggregated.
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
✓
Use a materialized view that pre-aggregates the data.
Clustering on the GROUP BY columns improves data locality, reducing shuffle during aggregation, while a materialized view that pre-aggregates can avoid computing the aggregation altogether for matching queries. Both directly target the aggregation bottleneck. Scaling up or enabling Search Optimization Service do not address the core issue of data organization for aggregation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a materialized view that pre-aggregates the data.
Why this is correct
A materialized view that pre-aggregates the data can eliminate the need to compute the aggregation at query time, especially if the query matches the view's definition. This can dramatically reduce query latency. However, it requires storage and maintenance, and is only effective if the query patterns are repetitive and align with the view's aggregation.
- ✗
Rewrite the query to use a window function instead of GROUP BY.
Why it's wrong here
Rewriting to a window function does not reduce the amount of data processed; it may even increase complexity. Window functions are useful for different purposes, such as ranking or running totals, but they do not inherently improve aggregation performance. The bottleneck is the aggregation itself, so this change would not address the issue.
- ✗
Increase the size of the virtual warehouse to add more compute resources.
Why it's wrong here
Increasing warehouse size provides more compute power, which can speed up aggregation by parallelizing the work. However, it does not address the underlying data distribution. If the aggregation is bottlenecked by data shuffling or skew, a larger warehouse may not help proportionally and increases cost. It is a general scaling option, not a targeted optimization.
- ✓
Add a clustering key on the columns used in the GROUP BY clause.
Why this is correct
Clustering on the GROUP BY columns can co-locate similar values, reducing the amount of data shuffled during aggregation. This allows Snowflake to perform partial aggregations more efficiently, as data for each group is stored together. It can significantly speed up aggregation queries, especially when the grouping columns have moderate cardinality.
- ✗
Enable the Search Optimization Service on the fact table.
Why it's wrong here
Search Optimization Service is designed for point lookups and selective filters, not for accelerating aggregations. It can speed up queries with highly selective predicates, but it does not optimize the aggregation operator. In this scenario, the bottleneck is aggregation, so SOS would not help and could add overhead.
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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
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