ARA-C01 Performance Optimization Practice Question
A Snowflake architect is investigating a query that performs poorly due to a large hash join. The query joins a 5TB fact table with a 10GB dimension table. The fact table is not clustered. The query filters the fact table on a date range that covers 10% of the data. Which optimization is most likely to improve performance with minimal cost?
⚠ Common exam trap
The trap here is assuming that increasing warehouse size will solve the performance issue, when the real problem is the amount of data being scanned due to lack of pruning.
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
✓
Add a clustering key on the date column of the fact table.
Clustering the fact table on the date column enables micro-partition pruning for the date range filter, reducing the data scanned by the join. This directly addresses the performance bottleneck without the overhead of larger warehouses or materialized views. It is the most cost-effective optimization for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a clustering key on the date column of the fact table.
Why this is correct
Clustering on the date column allows Snowflake to prune micro-partitions that fall outside the filtered date range, reducing the data scanned by the join. Since the query filters on date and only 10% of data is relevant, clustering can significantly reduce I/O and improve performance. It is a targeted and cost-effective optimization for this scenario.
- ✗
Increase the warehouse size to provide more memory for the hash join.
Why it's wrong here
A larger warehouse provides more memory and compute, which can speed up the hash join, but it does not reduce the amount of data that must be scanned. Since the fact table is not clustered, the join still processes all 5TB of data before filtering. This approach increases cost without addressing the root cause.
- ✗
Create a materialized view that pre-joins the fact and dimension tables.
Why it's wrong here
Materialized views are not designed to pre-join large fact tables with dimensions because the result would be nearly as large as the fact table itself, leading to high storage and maintenance costs. Snowflake also has restrictions on materialized views with joins. This is not a practical or cost-effective solution.
- ✗
Enable the Search Optimization Service on the fact table's date column.
Why it's wrong here
Search Optimization Service is designed to accelerate point lookups and certain types of queries, but it is not effective for range filters like a date range covering 10% of data. It adds storage and maintenance overhead and would not provide the same pruning benefits as clustering for this range predicate.
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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
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.