DAA-C01 Exam Dumps | A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion

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Question 95/151

A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?

A. Create a materialized view joining the two tables and filter on 'SALE_DATE. Refresh the materialized view daily.

B. Create a standard index on 'SALES.CUSTOMER ID and 'CUSTOMERS.CUSTOMER ID.

C. Create a search optimization on 'SALES.CUSTOMER and 'CUSTOMERS.CUSTOMER ID.

D. Create a cluster key on 'SALES' table on 'CUSTOMER_ID' and re-cluster the table after data loading.

E. Create a 'SALES LAST 30 DAYS' table containing only the last 30 days of sales, and join that table to the 'CUSTOMERS table. Refresh the 'SALES LAST 30 DAYS daily.

Correct Answer: E

Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.

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