COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
Exhibit
{ "QueryProfile": { "Operator": "Join", "Details": { "JoinType": "Inner", "Condition": "None (Cartesian Product)", "RowsProduced": 15000000000, "PercentageOfTotalTime": 85 } } }Refer to the exhibit. Based on the Query Profile snippet, which optimization strategy would most likely address the high execution time and massive row production?
⚠ Common exam trap
Candidates often try to resolve massive row explosions by scaling up the virtual warehouse or adding clustering keys, ignoring the root structural issue which is an accidental Cartesian product.
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
✓
Review the SQL to ensure a valid join predicate exists between the tables.
The exhibit identifies a Cartesian product join, which occurs when a join condition is missing or improperly defined, resulting in every row from one table being combined with every row from another. This leads to exponential data growth and severe performance issues. Correcting the join logic is the only way to prevent the system from generating these massive, unnecessary intermediate datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a clustering key to the primary keys of both tables.
Why it's wrong here
Clustering keys assist with partition pruning during the scanning phase but cannot mitigate the performance impact of a Cartesian product. Once the data is scanned, the join operator will still produce an excessive number of rows because the logical relationship between the tables remains incorrectly defined in the SQL.
- ✗
Enable the Query Acceleration Service for the warehouse.
Why it's wrong here
The Query Acceleration Service offloads heavy scanning and filtering tasks to shared compute resources, but it is not intended to fix inefficient SQL. A Cartesian product creates a processing bottleneck at the join level that additional compute power cannot efficiently resolve without first fixing the underlying query logic.
- ✓
Review the SQL to ensure a valid join predicate exists between the tables.
Why this is correct
A Cartesian product indicates that the SQL lacks a restrictive join condition, causing an explosion in the result set size. By defining a proper predicate, the optimizer can use more efficient join algorithms like Hash Joins, drastically reducing the number of rows processed and the total execution time.
- ✗
Increase the warehouse size to 4X-Large to handle the volume.
Why it's wrong here
Scaling the warehouse provides more resources, but it is a costly and temporary workaround for a logical error in the code. Even a large warehouse will struggle with the exponential row growth of a Cartesian product, making code correction a far more effective and sustainable optimization strategy.
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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
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