An engineer runs SYSTEM$CLUSTERING_INFORMATION on a fact table and sees an average clustering depth close to 1 for the clustering column. They must explain to the team what that low depth indicates about how the data is organised on that column. Which interpretation is correct?
- AFew micro-partitions overlap on that column's value ranges, so the data is well clustered and queries on it prune effectively. Correct
- BThe clustering key was rejected, so Snowflake silently stopped maintaining any physical ordering and reverted the table to fully unordered load order.
- COnly one micro-partition exists for the whole table, so clustering depth cannot grow above 1 regardless of how much data is loaded.
- DAlmost every micro-partition overlaps on that column's value ranges, so the table is badly clustered and most partitions must be scanned.
Why A is correct: A clustering depth approaching 1 means very few micro-partitions share overlapping value ranges for that column, which is the ideal state for pruning and confirms strong clustering.
Why B is wrong: Snowflake does not silently reject a clustering key, and a depth near 1 reflects good organisation rather than abandoned maintenance, so this misreads the metric.
Why C is wrong: Depth near 1 measures overlap across many partitions, not a single-partition table, and large tables hold many micro-partitions, so this confuses depth with partition count.
Why D is wrong: Heavy overlap and poor pruning correspond to a high clustering depth, not a depth near 1, so this inverts the meaning of the metric the engineer is reading.