ARA-C01 Performance Optimization Practice Question
An architect is investigating a performance regression in a query that previously ran quickly. The query involves a join between a large fact table and a small dimension table. The query profile shows a significant amount of time spent in the 'Join' operator, with many rows being processed. Which of the following is the most likely cause of the performance degradation?
⚠ Common exam trap
The trap here is assuming that join performance issues are always due to warehouse size or statistics, when a change in table size can alter the join strategy and cause shuffling.
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
✓
The small dimension table has grown significantly and is no longer suitable for a broadcast join.
When a small dimension table grows, it may exceed the threshold for a broadcast join, causing the optimizer to switch to a hash join that shuffles the large fact table. This increases data movement and join processing time. The other options would typically manifest in different operators or with additional symptoms like spills.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The warehouse size was reduced, causing less memory for join operations.
Why it's wrong here
A smaller warehouse would affect all operations, not just the join. While it could cause spills, the query profile would likely show spillage to local or remote storage. The specific increase in Join operator time with many rows processed suggests a change in data volume or join strategy, not just compute resources.
- ✗
Statistics on the fact table are stale, leading to a poor join order.
Why it's wrong here
Stale statistics can lead to suboptimal join order, but Snowflake automatically collects statistics and does not rely on manual updates. The optimizer uses metadata and runtime information. While join order can be a factor, the more direct cause of increased Join operator time is a change in table size affecting join strategy.
- ✓
The small dimension table has grown significantly and is no longer suitable for a broadcast join.
Why this is correct
If the dimension table has grown, the optimizer may no longer choose a broadcast join, which is efficient for small tables. Instead, it might perform a hash join that requires shuffling the large fact table, leading to increased time in the Join operator. This is a common cause of performance regression in such scenarios.
- ✗
The fact table has been clustered on a different column, causing poor pruning.
Why it's wrong here
Poor pruning would increase the time spent in the TableScan operator, not specifically the Join operator. The query profile would show more partitions scanned, but the join itself would not necessarily be slower if the join keys are properly distributed. The symptom points to join processing, not scanning.
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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.