Answer: Clustering depth

Clustering depth is the correct metric for monitoring table health in Snowflake because it measures the average number of micro-partition overlaps for a clustering key. A lower clustering depth indicates more effective clustering, leading to better query pruning and performance. As DML operations occur over time, monitoring clustering depth helps detect when a table's physical data layout degrades and may require reclustering. The community discussion shows 100% consensus on option A, with detailed reasoning that clustering depth directly reflects clustering effectiveness, while other options like clustering key (B) is a design choice, total partition count (C) is a structural metric, and total number of rows (D) is a volume metric, none of which directly indicate clustering health.

Author: LeetQuiz Editorial Team