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Answer: Use a smaller number of larger tables rather than a larger number of smaller tables.
The question asks for a recommended approach for optimizing query performance in Snowflake. Option D is correct because using a smaller number of larger tables reduces data movement during joins, improves caching efficiency, and simplifies query planning, as supported by the community discussion with high upvotes (4 upvotes) and detailed reasoning about clustering and partition pruning. Option A is incorrect because subqueries are not always optimal and can sometimes hinder performance if not used appropriately. Option B is incorrect as excessive joins increase complexity and data shuffling, degrading performance. Option C is incorrect because selecting unnecessary columns increases data transfer and processing overhead, contrary to best practices.
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What are recommended approaches for optimizing query performance in Snowflake?
A
Use subqueries whenever possible.
B
Use a large number of joins to combine data from multiple tables.
C
Select all columns from tables, even if they are not needed in the query.
D
Use a smaller number of larger tables rather than a larger number of smaller tables.