COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A data engineer is investigating a slow-running query. The Query Profile shows a high percentage of time spent in the TableScan operator and a large number of partitions scanned. The engineer wants to reduce the number of partitions scanned by using pruning. Which TWO actions should the engineer take? (Choose two.)
⚠ Common exam trap
The trap here is thinking that warehouse size or table type affects the number of partitions scanned, when pruning is determined by the query predicates and data organization.
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
✓
Apply filters early in the query and avoid wrapping filter columns in functions.
Partition pruning is driven by filters that the optimizer can use against micro-partition metadata. Filtering on clustering key columns and avoiding functions on filter columns allow Snowflake to skip irrelevant micro-partitions, directly reducing the number of partitions scanned.
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 filters early in the query and avoid wrapping filter columns in functions.
Why this is correct
Filters that are applied directly to columns allow the optimizer to use them for partition pruning. Wrapping a filter column in a function, such as UPPER or CAST, can prevent the optimizer from using the column's metadata for pruning. Applying filters early and keeping them as simple predicates on columns helps Snowflake eliminate unnecessary micro-partitions, reducing the number of partitions scanned.
- ✗
Increase the size of the virtual warehouse to reduce the number of partitions scanned.
Why it's wrong here
Increasing the warehouse size adds more compute resources, which can improve performance for some workloads, but it does not reduce the number of partitions scanned. The number of partitions scanned is determined by pruning, which depends on the query filters and data organization. A larger warehouse may process the same number of partitions faster, but it does not change the volume of data read.
- ✗
Use the SEARCH function in the WHERE clause for all string filters.
Why it's wrong here
The SEARCH function is part of the search optimization service and is used for substring searches. It can accelerate certain queries, but it is not a general replacement for pruning. Using it for all string filters may not reduce partitions scanned and could add overhead. Pruning is primarily achieved through filters on clustering keys or natural ordering of data, not by using SEARCH indiscriminately.
- ✗
Convert the table to a transient table to enable automatic pruning.
Why it's wrong here
Transient tables are a table type that affects data retention and fail-safe, not pruning. Converting a table to transient does not change how micro-partitions are pruned. Pruning is based on the filters and clustering metadata, regardless of table type. This action would not reduce the number of partitions scanned and could have unintended data retention consequences.
- ✓
Ensure that the query filters on columns that are part of the clustering key, if one exists.
Why this is correct
Clustering keys organize data so that similar values are stored in the same micro-partitions. When a query filters on a clustering key column, Snowflake can use the cluster metadata to prune micro-partitions that do not contain the filtered values. This reduces the number of partitions scanned and improves performance. Ensuring the filter aligns with the clustering key is a direct way to enable effective pruning.
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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 COF-C03 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 COF-C03 exam.