ARA-C01 Performance Optimization Practice Question
A Snowflake architect is investigating a slow-running query that joins a large fact table with a small dimension table. The query profile shows that the join operation is using a Cartesian product, resulting in a massive number of rows processed. The architect checks the query and notices that the join condition is missing. Which action should the architect take to resolve the performance issue?
⚠ Common exam trap
The trap here is thinking that adding a filter or scaling up the warehouse can mitigate a Cartesian join, when the only real solution is to correct the join condition.
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
✓
Rewrite the query to include an appropriate join condition between the fact and dimension tables.
A Cartesian product in a join is typically caused by a missing or incorrect join condition. The most effective fix is to rewrite the query to include the proper join predicate, which enables Snowflake to execute an efficient join algorithm. Other measures like adding filters or increasing warehouse size do not address the core problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the Query Acceleration Service to automatically optimize the join.
Why it's wrong here
Query Acceleration Service (QAS) is designed to offload portions of a query to shared compute resources, but it does not rewrite queries or add missing join conditions. It can help with scans and certain operations, but a Cartesian product is a logical error that QAS cannot correct. The query would still be inefficient.
- ✓
Rewrite the query to include an appropriate join condition between the fact and dimension tables.
Why this is correct
A Cartesian product occurs when a join lacks a proper join condition, causing every row from one table to be paired with every row from the other. Adding the correct join condition (e.g., matching foreign key to primary key) eliminates the Cartesian product and allows Snowflake to perform an efficient hash join or similar operation, drastically reducing the number of rows processed.
- ✗
Add a WHERE clause to filter the result set after the join.
Why it's wrong here
Adding a WHERE clause after a Cartesian join does not prevent the Cartesian product from being generated. The join still processes all combinations of rows, which is highly inefficient. The WHERE clause would filter the output but would not address the root cause of the performance issue, which is the missing join condition.
- ✗
Increase the size of the virtual warehouse to handle the larger result set.
Why it's wrong here
Increasing warehouse size provides more compute resources but does not fix the underlying query logic. A Cartesian product will still generate an enormous number of rows, potentially overwhelming even a larger warehouse. The query would remain slow and resource-intensive because the fundamental issue is the missing join predicate.
Visual reference
About these practice questions
Courseiva writes every ARA-C01 question from scratch — 209 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.