What action could a Data Engineer take to increase the query performance of an external table?

Question

Options

• AAdd a clustering key to the external table.
• BEnable the search optimization service on the external table.
• CCreate a materialized view on the external table.
• DSet the USE_CACHED_RESULTS parameter to True for the external table.

• A
  12% (3)
• B
  4% (1)
• C
  81% (21)
• D
  4% (1)

Explanation

Creating a materialized view on an external table works because it pre-computes and physically stores query results in Snowflake's internal storage, so subsequent queries read optimized, cached data rather than scanning raw external files every time - this is the primary supported strategy for improving external table performance in Snowflake. Clustering keys (A) cannot be applied to external tables at all; clustering is only supported on native internal tables where Snowflake controls the micro-partition layout. The search optimization service (B) is likewise unsupported for external tables - it only accelerates point lookups on internal tables. USE_CACHED_RESULTS (D) is a session-level parameter, not a table-level one, and result caching on external tables is unreliable since Snowflake can't guarantee the underlying external data hasn't changed.

Memory tip: Think of external tables as "read-only windows into S3/cloud storage" - Snowflake can't reorganize that data (no clustering, no search optimization), but it can create an internal materialized snapshot of it via a materialized view, which is the one lever you have.

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