ARA-C01 Performance Optimization Practice Question
A Snowflake architect notices that a recurring ETL job that loads data into a table and then immediately runs a complex aggregation query is taking longer than expected. The table is not clustered, and the query filters on a timestamp column. Which action would most directly improve the performance of the aggregation query?
⚠ Common exam trap
The trap here is assuming that increasing warehouse size always solves performance issues, when in fact reducing data scanned through clustering can be more effective for filtered aggregations.
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
✓
Adding a clustering key on the timestamp column
Clustering the table on the timestamp column enables partition pruning, so the query only scans micro-partitions that contain the relevant time range. This directly reduces I/O and improves aggregation performance. Search Optimization is for point lookups, larger warehouses add compute but not pruning, and multi-cluster addresses concurrency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Adding a clustering key on the timestamp column
Why this is correct
Clustering the table on the timestamp column physically orders the data by that column, allowing the query to prune micro-partitions based on the timestamp filter. This reduces the amount of data scanned, directly improving the performance of the aggregation query that filters on that column.
- ✗
Increasing the warehouse size
Why it's wrong here
Increasing warehouse size adds more compute resources, which can speed up query execution, but it does not reduce the amount of data that needs to be scanned. If the query is I/O-bound due to scanning many micro-partitions, a larger warehouse may not help as much as clustering. It is a less targeted solution.
- ✗
Using a multi-cluster warehouse
Why it's wrong here
A multi-cluster warehouse helps with concurrency by adding clusters when many queries are queued, but it does not improve the performance of a single query. The ETL job's aggregation query is a single query, so multi-cluster would not reduce its execution time.
- ✗
Enabling the Search Optimization Service on the table
Why it's wrong here
Search Optimization Service is designed to speed up highly selective point lookups and not range-based aggregations. It maintains a search access path for equality and IN filters, but it does not optimize aggregation queries that scan large portions of the table. Thus, it would not directly improve the described aggregation.
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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.