According to the SnowPro Advanced: Architect documents and learning resources, the best way to

enable optimal clustering to enhance performance for different access paths on a given table is to

create multiple materialized views with different cluster keys. A materialized view is a pre-computed

result set that is derived from a query on one or more base tables. A materialized view can be

clustered by specifying a clustering key, which is a subset of columns or expressions that determines

how the data in the materialized view is co-located in micro-partitions. By creating multiple

materialized views with different cluster keys, an Architect can optimize the performance of queries

that use different access paths on the same base table. For example, if a base table has columns A, B,

C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can

create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way,

each query can leverage the optimal clustering of the corresponding materialized view and achieve

faster scan efficiency and better compression.

Reference:

Snowflake Documentation: Materialized Views

Snowflake Learning: Materialized Views

https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performanceproblems/