What tables are clustering useful for?

• A. Tables that are updated frequently
• B. Tables that are never queried
• C. Tables that are queried frequently and NOT changed frequently
• D. Tables with no indexing

Answer: (C)

Clustering helps Snowflake prune data efficiently by organizing a table on a clustering key, so queries can skip large portions of data that don’t match the filter conditions. The biggest gains come when you have a large table that people query often with selective filters on that clustering key, but the table isn’t updated frequently. In that scenario, the clustering key keeps data physically nearby for the common query patterns, so less data is scanned and responses come back faster.

If a table is updated often, maintaining the clustering structure adds overhead because Snowflake has to reorganize data as new rows are added or existing ones change. That maintenance can erode the performance benefits, making clustering less worthwhile. So the scenario that best fits clustering is tables that are queried frequently and not changed frequently.

A table that is never queried won’t gain much from clustering, since there’s little to prune. And clustering isn’t about traditional indexing in Snowflake—the benefit comes from data organization for query pruning, not from an index structure.