ARA-C01 Performance Optimization Practice Question
A Snowflake architect is reviewing a query that uses a window function partitioned by customer_id and ordered by transaction_date. The query processes a 1TB table and runs slowly. The architect notices that the table is not clustered. Which action should the architect take to improve performance?
⚠ Common exam trap
The trap here is assuming that increasing warehouse size will always improve window function performance, when data clustering is often more impactful for large-scale sorts and partitions.
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
✓
Cluster the table on customer_id and transaction_date.
Clustering on the columns used in the window function's PARTITION BY and ORDER BY clauses co-locates related data, reducing shuffling and sorting during query execution. This directly improves the performance of window functions on large tables. Other options either do not address data organization or are not applicable.
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 warehouse size to provide more compute for the window function.
Why it's wrong here
A larger warehouse provides more compute, which can speed up the window function, but it does not address the underlying data organization. Without clustering, the data may still need to be shuffled and sorted extensively, leading to inefficiency. This approach increases cost without optimizing the data layout.
- ✗
Use the Search Optimization Service on the customer_id column.
Why it's wrong here
Search Optimization Service is designed for point lookups and certain equality predicates, not for window functions that require sorting and partitioning. It would not help with the window function's need to process data in a specific order. It adds overhead without addressing the core performance issue.
- ✓
Cluster the table on customer_id and transaction_date.
Why this is correct
Clustering on the partition and order columns of the window function allows Snowflake to co-locate related rows, reducing data shuffling and improving the efficiency of the window computation. This can significantly speed up the query by minimizing the amount of data that must be sorted and processed within each partition.
- ✗
Create a materialized view that pre-computes the window function.
Why it's wrong here
Materialized views can pre-compute some aggregations, but they do not support window functions directly. Even if they did, the materialized view would need to be refreshed, adding overhead. This is not a supported or practical optimization for window functions in Snowflake.
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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.