Answer: Assess the average table depth to identify how clustering is impacting the query.

The question asks how clustering should be used to optimize query performance on a very large table. Option D is correct because it emphasizes assessing clustering effectiveness through the SYSTEM$CLUSTERING_INFORMATION function, which evaluates average table depth to determine if data is well-clustered. This aligns with Snowflake best practices for monitoring and optimizing clustering. Option C is incorrect because it suggests using columns from SELECT clauses, but clustering keys should prioritize columns used in WHERE clauses (selective filters) for pruning efficiency, not SELECT clauses. The community discussion strongly supports D (57% vs. 43% for C), with multiple comments noting that SELECT clauses are irrelevant for clustering optimization, and WHERE clauses are what matter. Options A and B are suboptimal: A involves manual intervention without assessment, and B may lead to poor clustering if the high cardinality column isn't used in filters.

Author: LeetQuiz Editorial Team