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Question 634 - COF-C02 discussion

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How does Snowflake utilize clustering information to improve query performance?

A.
It prunes unnecessary micro-partitions based on clustering metadata.
Answers
A.
It prunes unnecessary micro-partitions based on clustering metadata.
B.
It compresses the data within micro-partitions for faster querying.
Answers
B.
It compresses the data within micro-partitions for faster querying.
C.
It automatically allocates additional resources to improve query execution.
Answers
C.
It automatically allocates additional resources to improve query execution.
D.
It organizes clustering information to speed-up data retrieval from storage
Answers
D.
It organizes clustering information to speed-up data retrieval from storage
Suggested answer: A

Explanation:

Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.

Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.

Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.

References:

Snowflake Documentation on Clustering

Snowflake Documentation on Micro-partition Pruning

asked 23/09/2024
Tanpisit Songvechkasam
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