ARA-C01 Performance Optimization Practice Question
An architect is analyzing a Snowflake query that performs poorly. The Query Profile shows a high percentage of time spent in the 'TableScan' operator with a large number of partitions scanned, despite a filter on a column with high selectivity. The table is not clustered. Which action is most likely to improve performance?
⚠ Common exam trap
The trap here is thinking that increasing warehouse size or enabling Query Acceleration Service will always fix slow scans, but they do not address the root cause of scanning unnecessary partitions.
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
✓
Add a clustering key on the filtered column.
The query scans many partitions because the table is not clustered, even though the filter is selective. Adding a clustering key on the filtered column reorganizes data so that only relevant micro-partitions are read. This reduces I/O and TableScan time. Other options may provide some benefit but do not directly solve the partition pruning issue.
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 on the warehouse.
Why it's wrong here
Query Acceleration Service offloads portions of query processing to shared compute resources, but it is most effective for queries with large scans and selective filters that can be parallelized. It may help, but it does not reorganize data to reduce partitions scanned. Clustering is a more direct solution for partition pruning.
- ✓
Add a clustering key on the filtered column.
Why this is correct
Clustering on the filtered column will co-locate similar values in the same micro-partitions, enabling Snowflake to prune partitions that do not match the filter. This reduces the number of partitions scanned, directly addressing the high TableScan time. It is the most targeted fix for this scenario.
- ✗
Create a materialized view on the filtered column.
Why it's wrong here
A materialized view can pre-compute results, but it does not change the underlying table's partition pruning. For a highly selective filter, the base table still needs to be scanned unless the materialized view fully covers the query. Materialized views are also limited in supported operations and may not be suitable for all queries.
- ✗
Increase the warehouse size to add more compute nodes.
Why it's wrong here
A larger warehouse adds compute power, which can speed up processing, but if the query is I/O-bound due to scanning many partitions, the improvement may be limited. It does not reduce the amount of data read. Clustering reduces the data scanned, making it a more efficient and cost-effective solution.
About these practice questions
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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 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.