DEA-C02 Exam Dumps | A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' ,

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Question 69/149

A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A. Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.

B. Replace the standard JOINs with LATERAL FLATTEN operations.

C. Increase the virtual warehouse size to X-LARGE without analyzing the query profile.

D. Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.

E. Convert the entire dataset into a single VARIANT column and query using JSON path expressions.

Correct Answer: A,D

Materialized views can pre-compute the joined data and apply filters, significantly reducing the amount of data the query needs to process at runtime. Clustering keys on the frequently filtered columns in 'ORDERS' and 'CUSTOMERS' will enable data skipping, allowing Snowflake to efficiently prune micro-partitions. Creating indexes is not directly supported in Snowflake, however Clustering is equivalent to Indexing strategy. Increasing warehouse size without analysis is not an appropriate strategy. Lateral Flatten is relevant when dealing with array or semi- structured data, and converting the data into VARIANT would severely impact query performance.

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