ARA-C01 Performance Optimization Practice Question
An architect is optimizing a query that joins a large fact table and a small dimension table. The query is slow. What should be the first step to improve performance?
⚠ Common exam trap
Candidates often jump to rewriting the SQL query or changing the table structure before checking the actual execution plan, which is the most efficient way to diagnose join behavior.
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
✓
Analyze the Query Profile to check for broadcast joins.
In star schema joins, Snowflake's optimizer typically handles small dimension tables by broadcasting them to all nodes, which is very efficient. However, if the query is still slow, checking the Query Profile to see if the join is actually being broadcast is essential. If the optimizer is not broadcasting the small table, the architect can use a hint or reorganize the join to ensure it happens.
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 clustering key on the fact table.
Why it's wrong here
Clustering is helpful for range filtering, but it does not directly optimize join performance between a fact and dimension table. While important for overall table health, it is not the primary mechanism to address join performance issues, which are often related to data distribution and join implementation methods.
- ✓
Analyze the Query Profile to check for broadcast joins.
Why this is correct
The Query Profile reveals how the join is being performed. A broadcast join sends the small dimension table to every node, which is optimal. If it is not being broadcast, the architect can investigate why the optimizer chose a different method and potentially adjust the query to force better behavior.
- ✗
Increase the warehouse size to its maximum.
Why it's wrong here
Increasing the warehouse size unnecessarily increases costs and does not address the underlying join efficiency problem. Optimization should start by ensuring the query plan is optimal, rather than throwing more hardware at a problem that might be solved through better query structure or join strategy.
- ✗
Convert the dimension table into a temporary table.
Why it's wrong here
The temporary nature of a table has no impact on its suitability for broadcast joins. The optimizer cares about the statistics and size of the table, not its persistence type. Changing the table type will not change how the engine plans the join execution or improve the performance.
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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.