Which statement best describes clustering depth?

• A. The smaller the average depth, the better clustered the table is with respect to the specified columns.
• B. The larger the average depth, the better clustered the table is.
• C. The smaller the maximum depth, the better clustered the table is with respect to the specified columns.
• D. Clustering depth has no impact on performance.

Answer: (A)

Clustering depth measures how tightly data is organized by the clustering keys across micro-partitions. When the average depth is small, data tends to align more closely with the clustering order, allowing Snowflake to prune a larger portion of partitions during queries that filter on those columns. This means less data to scan and faster performance. So, the statement that the smaller the average depth, the better clustered the table is describes the practical impact of clustering depth on query efficiency. If the average depth is higher, clustering is less effective and more partitions may be scanned. The maximum depth is not the primary measure of overall clustering quality, and saying clustering depth has no impact on performance is inaccurate because better clustering directly improves pruning and scan cost.