Which statement about clustering depth is correct?

• A. It measures the average depth of overlapping micro-partitions for specified columns.
• B. It measures the total number of micro-partitions in the table.
• C. It measures the age of data in partitions.
• D. It measures the system load.

Answer: (A)

Clustering depth measures how well a clustering key groups data across Snowflake’s micro-partitions. It’s defined as the average depth of overlapping micro-partitions for the specified clustering columns—the idea being how many partitions, on average, contain the data for those key values. This tells you how effectively queries can prune partitions: a low depth means data is tightly aligned with the clustering keys and pruning is efficient, while a high depth indicates scattered data across many partitions and less effective pruning.

The other statements don’t describe this concept. It isn’t the total number of micro-partitions, the age of data in partitions, or the system load.