DAA-C01 Exam Dumps | A Snowflake data analyst is tasked with optimizing the performance of a frequently executed query. The

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Question 126/151

A Snowflake data analyst is tasked with optimizing the performance of a frequently executed query. The execution plan reveals a 'TableScan' operation on a large table named 'SALES DATR. The 'SALES DATA table is clustered on the 'SALE DATE' column. However, the query predicate uses a range filter on a different column, 'REGION', which is not part of the clustering key. Which of the following strategies would likely improve the query performance significantly? (Select TWO)

A. Recluster the table using both 'SALE_DATE' and 'REGION' as clustering keys to improve data locality for the filter.

B. Create a new clustering key using 'REGION' and removing the 'SALE DATE clustering key. Dropping the existing clustering key before creating a new one.

C. Create a search optimization service on the 'REGION' column of the 'SALES_DATA' table.

D. Increase the virtual warehouse size used for querying the 'SALES DATA' table.

E. Create a materialized view that includes both ' REGION' and necessary columns and only include the data from last one year to reduce table size in the view.

Correct Answer: C,E

Options C and E are the most impactful. Search optimization (C) allows Snowflake to efficiently prune the search space based on the REGION' filter. Creating a materialized view (E) allow precalculating the result and reducing data amount to scan for the query. Reclustering (A and B) may help in some cases. But the reclustering would not be the optimal choice. Increasing warehouse size (D) might provide a marginal improvement but does not directly address the inefficient table scan.

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