SOL-C01 Question #128: Real Exam Question with Answer & Explanation

Question

A Snowflake data warehouse has a large table named 'ORDERS' that is frequently queried by multiple virtual warehouses of different sizes for various analytical workloads. The data in the 'ORDERS' table is constantly being updated through batch loading processes. Which of the following strategies can be used to optimize query performance and minimize contention between different workloads, while ensuring data consistency?

Options

• ACreate multiple clones of the 'ORDERS' table, one for each virtual warehouse, to isolate workloads
• BUse Snowflake's caching mechanisms and query acceleration service to improve query
• CImplement data partitioning on the 'ORDERS' table based on a frequently queried column to
• DUtilize Snowflake's multi-cluster warehouses with auto-scaling enabled to handle concurrent
• ECreate multiple materialized views on the 'ORDERS' table tailored to the specific query patterns of

Explanation

Options B, D, and E are correct. Using caching and the query acceleration service (B) improves query performance by leveraging Snowflake's internal optimizations. Utilizing multi-cluster warehouses with auto-scaling (D) provides the necessary resources to handle concurrent queries without contention. Creating materialized views (E) tailored to specific query patterns pre- computes and stores the results, significantly reducing query execution time. Option A, cloning the table, consumes extra storage and isn't suitable for frequently updated data. Option C is an incorrect solution, as Snowflake does not directly support data partitioning by the user. Clustering can be used, but it isn't mentioned, and partitioning is wrong.

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