Which Snowflake tasks will take advantage of underlying micro-partition metadata?

The correct answers are A. Query pruning and E. Operations using Data Manipulation Language, or DML .

Snowflake automatically gathers and maintains metadata about micro-partitions. This metadata includes information such as the range of values in columns, distinct value counts, and other statistics. Snowflake uses this metadata to optimize query execution and table operations.

Why A is correct:

Query pruning is one of the most important uses of micro-partition metadata. Snowflake can skip micro-partitions that do not contain values needed by a query predicate.

Example:

SELECT *

FROM sales

WHERE sale_date = ' 2026-01-01 ' ;

If Snowflake knows from micro-partition metadata that certain micro-partitions do not contain sale_date = ' 2026-01-01 ' , those micro-partitions can be skipped.

Why E is correct:

DML operations such as UPDATE, DELETE, and MERGE can also benefit from micro-partition metadata. Snowflake can use metadata to identify which micro-partitions are affected by the operation instead of scanning unnecessary partitions.

Why the other options are incorrect:

B. The result cache returns previously computed query results. It avoids re-executing the query and therefore does not rely on micro-partition pruning in the same way.

C. Local disk cache stores data on warehouse compute resources after access, but it is not the main feature that uses micro-partition metadata.

D. “Remote disk cache” is not the standard Snowflake concept tested here. Snowflake has remote storage, warehouse cache, and result cache, but query pruning is the metadata-driven feature.

Official Snowflake documentation reference:

Snowflake documentation explains that micro-partition metadata is used for efficient query pruning and optimization. It also supports efficient table maintenance and DML operations by helping Snowflake identify relevant micro-partitions.