COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
Which approach is most effective for optimizing an aggregation query that performs a 'GROUP BY' on a high-cardinality column?
⚠ Common exam trap
Candidates often suggest increasing warehouse size as a first step, ignoring that materialized views specifically address high-cardinality aggregation bottlenecks more efficiently than scaling compute resources alone.
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
✓
Use a materialized view to pre-aggregate the data.
High-cardinality columns can cause memory bottlenecks during aggregation because the distinct values cannot fit into the memory of a single node, leading to disk spilling. By using techniques like pre-aggregation or creating a materialized view that groups the data by the high-cardinality column, you reduce the workload. These strategies move the compute-heavy grouping operation to a more efficient time or structure, thereby preventing memory exhaustion and significantly improving the performance of the aggregation query.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the warehouse size to add more nodes.
Why it's wrong here
While adding more nodes can help by distributing the data, it does not solve the underlying memory limitation for the individual grouping operations if they are not partitioned correctly. Scaling up to a larger warehouse is a reactive measure that may not yield the best performance-to-cost ratio compared to architectural changes.
- ✓
Use a materialized view to pre-aggregate the data.
Why this is correct
Materialized views automatically maintain pre-aggregated data. When the query is run, Snowflake can often leverage these pre-computed results instead of performing the expensive 'GROUP BY' on the raw, high-cardinality column at runtime. This drastically reduces CPU and memory usage, leading to much faster performance for the analytical query.
- ✗
Reduce the number of columns in the SELECT clause.
Why it's wrong here
Reducing columns in the SELECT clause helps reduce data transfer, but it does not influence the core bottleneck of the 'GROUP BY' operation on a high-cardinality column. The complexity and memory requirements of the aggregation remain largely unchanged regardless of how many other columns are included in the output.
- ✗
Change the table type to transient.
Why it's wrong here
Transient tables are designed to reduce storage costs and offer different data retention policies compared to permanent tables. They provide no performance benefits regarding query execution, aggregation, or memory management. Changing the table type is an administrative decision that has no impact on the performance of an aggregation query.
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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.