A Snowflake user wants to optimize performance for a query that queries only a small number of rows in a table. The rows require significant processing. The data in the table does not change…

Question

Options

• AAdd a clustering key to the table.
• BAdd the search optimization service to the table.
• CCreate a materialized view based on the query.
• DEnable the query acceleration service for the virtual warehouse.

• A
  7% (2)
• B
  4% (1)
• C
  70% (19)
• D
  19% (5)

Explanation

A materialized view pre-computes and stores query results, making repeated execution of complex, processing-heavy queries nearly instant. It is the best fit here because: the data changes infrequently (so the materialized view stays fresh without excessive refresh cost), the query targets a small row set with significant computation (exactly what materialized views are designed for). Search Optimization (B) speeds up point-lookup row retrieval but doesn't reduce processing overhead. Clustering keys (A) help with range scans on large tables. Query Acceleration (D) helps with warehouse-level parallelism for ad-hoc analytical queries, not reducing repeated computation.

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