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Question 151 - ARA-C01 discussion

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A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.

The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.

What step can be taken to improve the pruning of the reporting tables?

A.
Eliminate the use of Snowpipe and load the files into internal stages using PUT commands.
Answers
A.
Eliminate the use of Snowpipe and load the files into internal stages using PUT commands.
B.
Increase the size of the virtual warehouse to a size 5X-Large.
Answers
B.
Increase the size of the virtual warehouse to a size 5X-Large.
C.
Use an ORDER BY <cluster_key (s) > command to load the reporting tables.
Answers
C.
Use an ORDER BY <cluster_key (s) > command to load the reporting tables.
D.
Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.
Answers
D.
Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.
Suggested answer: C

Explanation:

Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.

* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1


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asked 23/09/2024
chris bezuidenhout
35 questions
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