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Answer: Create a materialized view based on the query.
The correct answer is C because creating a materialized view is optimal for this scenario. The query targets a small subset of rows with significant processing requirements, and the underlying data changes infrequently. Materialized views precompute and store query results, allowing subsequent queries to retrieve pre-processed data directly, which significantly improves performance. Option A (clustering key) is less suitable as it optimizes data distribution for large scans, not small subsets with heavy processing. Option B (search optimization service) accelerates point lookups but doesn't address the computational overhead. Option D (query acceleration service) helps with large-scale data processing but is overkill for small row sets and doesn't precompute results like materialized views. The community discussion strongly supports C with 100% consensus and references to Snowflake documentation.
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A user has a table with infrequently updated data. Their query targets a small subset of rows that require substantial processing. Which action should the user take to optimize query performance?
A
Add a clustering key to the table.
B
Add the search optimization service to the table.
C
Create a materialized view based on the query.
D
Enable the query acceleration service for the virtual warehouse.