What does clustering depth measure?

• A. The average depth (1 or greater) of the overlapping micro-partitions for specified columns.
• B. The total number of micro-partitions in the table.
• C. The number of distinct values per column.
• D. The maximum depth of overlapping partitions.

Answer: (A)

Clustering depth measures how many micro-partitions, on average, overlap for the specified clustering columns. In Snowflake, data is stored in micro-partitions, and when you cluster by certain columns, rows can fall into more than one partition because their value ranges overlap. The clustering depth is the average number of these overlapping partitions that a row belongs to. A value close to 1 means partitions barely overlap and pruning during queries is efficient; higher values indicate more overlap and less efficient pruning, suggesting the clustering could be improved. This metric specifically reflects the average overlapping depth, not the total number of partitions or the number of distinct values, and not the maximum depth.