ARA-C01 Performance Optimization Practice Question
An organization has a 500TB table containing IoT sensor data. Users frequently run point-lookup queries filtering by a specific Sensor_ID, which has very high cardinality. The table is currently clustered by Event_Timestamp. Performance for these Sensor_ID lookups is poor. Which architectural change would provide the most cost-effective performance improvement for these specific queries?
⚠ Common exam trap
Candidates frequently recommend changing the clustering key for high-cardinality point lookups, failing to realize that clustering is ineffective for high-cardinality equality searches which instead require the Search Optimization Service.
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
✓
Enable the Search Optimization Service for the Sensor_ID column.
The Search Optimization Service is a background process that speeds up point lookup queries by creating an optimized data structure. Unlike clustering, which reorders the actual data, this service tracks values across micro-partitions. It is ideal for high-cardinality columns and queries using equality predicates or specific functions like LIKE, where traditional pruning methods might scan too many partitions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Re-cluster the table using Sensor_ID as the primary clustering key.
Why it's wrong here
Changing the clustering key to a high-cardinality column like Sensor_ID often results in poor clustering depth and high maintenance costs. It would also degrade the performance of existing queries that rely on the Event_Timestamp for range scans, making it a less flexible solution for varied query patterns on large datasets.
- ✓
Enable the Search Optimization Service for the Sensor_ID column.
Why this is correct
Enabling the Search Optimization Service creates a persistent search access path that allows the query optimizer to bypass scanning irrelevant micro-partitions. This is specifically designed for high-cardinality columns and point lookups, providing significantly faster response times for filtered queries without the need to manually manage or re-sort the underlying table data.
- ✗
Create a Materialized View that filters for the most active Sensor_IDs.
Why it's wrong here
Materialized Views are best suited for pre-aggregating data or simplifying complex joins rather than optimizing point lookups across a high-cardinality set. Maintaining a view for only active IDs would not solve the performance issue for the full range of sensors and would incur significant storage and compute costs for maintenance.
- ✗
Increase the Virtual Warehouse size to 4X-Large to improve scanning speed.
Why it's wrong here
Scaling up the warehouse provides more compute power and memory, but it does not address the underlying issue of excessive data scanning. While the query might run faster due to sheer brute force, it is not cost-effective as it requires paying for a much larger warehouse for every single point lookup query.
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.