DAA-C01 Exam Dumps | A data analyst is optimizing query performance for a large reporting dashboard that accesses a Snowflake

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Snowflake.DAA-C01.v2025-12-12.q151
Question 38

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Question 38/151

A data analyst is optimizing query performance for a large reporting dashboard that accesses a Snowflake table 'SALES DATA' with millions of rows. The dashboard includes several complex calculations and aggregations based on 'SALE DATE and 'PRODUCT ID' The analyst observes that the dashboard load time is unacceptably slow, even after implementing standard query optimization techniques. Considering Snowflake's caching mechanisms and query profile, which of the following actions would MOST effectively improve the dashboard's performance while minimizing cost?

A. Increase the virtual warehouse size to a larger configuration (e.g., from X-Small to Large) to ensure sufficient compute resources. This directly speeds up individual query execution.

B. Implement result caching by ensuring that the underlying queries are deterministic and have not been modified. No action is needed; Snowflake automatically manages result caching.

C. Create a materialized view that pre-calculates the aggregations needed by the dashboard. Refresh the materialized view periodically (e.g., daily) to maintain data freshness.

D. Partition the 'SALES_DATX table by 'SALE_DATE to reduce the amount of data scanned during query execution. This avoids unnecessary scans.

E. Implement query tags and monitor Snowflake query history using the 'QUERY HISTORY view to identify resource-intensive queries and optimize them using query rewriting or indexing techniques.

Correct Answer: C

Materialized views offer a significant performance boost by pre-calculating and storing the results of complex aggregations. This reduces the computational load during dashboard refreshes. While increasing virtual warehouse size (A) provides more resources, it's often more cost-effective to optimize queries. Result caching (B) is automatic but depends on query determinism and recent execution. Partitioning (D) is not directly applicable to Snowflake. Query tags and history (E) are helpful for analysis but don't directly speed up dashboard load times.

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