ARA-C01 Performance Optimization Practice Question
A financial services company uses Snowflake to analyze trade data. They have a large table TRANSACTIONS with a clustering key on TRADE_DATE. Queries that filter on TRADE_DATE and ACCOUNT_ID are performing well, but queries that filter only on ACCOUNT_ID are slow. The architect wants to improve performance for ACCOUNT_ID-only queries without degrading the performance of TRADE_DATE queries. Which solution is most appropriate?
⚠ Common exam trap
The trap here is assuming that you can have multiple clustering keys or that changing the clustering key is the only way to improve performance on a non-clustered column.
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
✓
Create a search optimization service on the ACCOUNT_ID column.
Search Optimization Service is the ideal solution for accelerating selective queries on columns that are not part of the clustering key. It creates a persistent search access path that allows Snowflake to quickly locate micro-partitions containing the desired values. This improves ACCOUNT_ID-only queries without altering the existing clustering on TRADE_DATE, thus preserving performance for TRADE_DATE queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a search optimization service on the ACCOUNT_ID column.
Why this is correct
Search Optimization Service (SOS) is designed to accelerate point lookups and selective queries on columns that are not the clustering key. By enabling SOS on ACCOUNT_ID, queries filtering on that column can benefit from an optimized search access path, without affecting the existing clustering on TRADE_DATE. This maintains performance for TRADE_DATE queries while improving ACCOUNT_ID queries.
- ✗
Add a secondary clustering key on ACCOUNT_ID.
Why it's wrong here
Snowflake does not support multiple clustering keys on a single table. A table can have only one clustering key, which can be composite. You cannot define a secondary clustering key. Therefore, this option is not feasible and would not address the performance issue.
- ✗
Add a materialized view that filters on ACCOUNT_ID and pre-aggregates the data.
Why it's wrong here
A materialized view can improve performance for specific queries, but it requires maintenance and consumes storage. It may not be suitable if the queries are highly selective and return many rows, as the materialized view would need to store all relevant data. Additionally, it does not leverage clustering and may not be as efficient as other options.
- ✗
Change the clustering key to (ACCOUNT_ID, TRADE_DATE).
Why it's wrong here
Changing the clustering key to (ACCOUNT_ID, TRADE_DATE) would improve ACCOUNT_ID-only queries but degrade TRADE_DATE-only queries because TRADE_DATE would no longer be the leading column. This does not meet the requirement of not degrading existing performance for TRADE_DATE queries.
About these practice questions
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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.