ARA-C01 Performance Optimization Practice Question
An architect needs to optimize a dashboard that frequently queries a multi-terabyte table. The queries involve complex aggregations on several columns and a join to a small dimension table. The dashboard allows users to filter by any combination of five different dimensions. Which optimization strategy is most appropriate?
⚠ Common exam trap
Candidates often suggest Search Optimization Service for aggregations, confusing it with point-lookup optimization, or they suggest clustering, which is less efficient for complex multi-dimensional aggregations than materialized views.
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
✓
Implement a Materialized View that pre-aggregates the data by the five dimensions.
Materialized Views are highly effective for queries that involve complex aggregations and joins on large datasets where the results can be pre-calculated. Unlike the Search Optimization Service, which is for point lookups, Materialized Views store the actual result of the query. This significantly reduces the compute required at runtime for dashboards with repetitive aggregation patterns.
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 Search Optimization on all five dimension columns.
Why it's wrong here
Search Optimization is intended for point lookups (finding specific rows) rather than improving the performance of complex aggregations or joins. While it might help if users filter for a single unique value, it will not speed up the sum, count, or group-by operations that are central to dashboard performance.
- ✓
Implement a Materialized View that pre-aggregates the data by the five dimensions.
Why this is correct
Materialized Views are ideal for this scenario because they can pre-calculate the aggregations across the required dimensions. When a user queries the dashboard, Snowflake can pull the pre-computed results directly from the view, avoiding the need to scan the multi-terabyte fact table and perform expensive calculations repeatedly for every user.
- ✗
Use a Cluster Key on the dimension table to speed up the joins.
Why it's wrong here
Dimension tables are typically small, and Snowflake's query optimizer usually handles joins with small tables efficiently using broadcast joins. Clustering a small dimension table would provide negligible performance gains compared to the massive overhead of aggregating a multi-terabyte fact table, which remains the primary bottleneck in this scenario.
- ✗
Increase the Warehouse size to reduce the time for aggregations.
Why it's wrong here
While a larger warehouse would speed up the aggregations, it is a reactive approach that consumes more credits for every query. It does not address the inefficiency of re-calculating the same aggregations over and over. Materialized Views provide a more permanent and cost-effective optimization for stable aggregation patterns.
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.