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Snowflake Exam ARA-R01 Topic 4 Question 17 Discussion

Actual exam question for Snowflake's ARA-R01 exam
Question #: 17
Topic #: 4
[All ARA-R01 Questions]

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?

Show Suggested Answer Hide Answer
Suggested Answer: C

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.

Reference =

* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1


by Janet at Sep 09, 2024, 12:11 AM

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Polly
11 months ago
That could also work, larger files might help with pruning efficiency.
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Sina
11 months ago
I'm not sure about that, maybe we should consider option D) Create larger files for Snowpipe to ingest.
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Yolando
11 months ago
I dunno, have they tried turning it off and on again? Seems like a classic IT problem to me.
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Carissa
10 months ago
Yeah, that might be a good solution to improve performance.
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Gladys
10 months ago
That could help with the pruning issue in the reporting tables.
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Annalee
11 months ago
Maybe they should try increasing the size of the virtual warehouse to a size 5X-Large.
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Casey
11 months ago
I agree with Polly, using ORDER BY can help improve pruning.
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Denna
11 months ago
You know, if they're really struggling with pruning, maybe they should just throw the whole reporting database away and start over. That's the *real* solution, right?
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Vivan
11 months ago
Option D sounds interesting. Bigger files and less frequent staging could help optimize the Snowpipe process. It's worth a shot!
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Emile
10 months ago
Let's give it a try and see if it improves the performance of the data pipeline.
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Rozella
10 months ago
I agree. Creating larger files for Snowpipe to ingest and adjusting the staging frequency could make a difference.
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Rolande
10 months ago
Yes, that could definitely help with the pruning issue in the reporting tables.
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Myong
11 months ago
Option D sounds interesting. Bigger files and less frequent staging could help optimize the Snowpipe process. It's worth a shot!
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Staci
11 months ago
Hmm, I'm not sure about that. Increasing the warehouse size seems like it might just be throwing more resources at the problem instead of addressing the root cause.
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Margurite
11 months ago
B: Yeah, that could definitely improve the performance without just increasing the warehouse size.
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Oliva
11 months ago
A: I think using an ORDER BY command might help with pruning the reporting tables more effectively.
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Polly
11 months ago
I think we should try option C) Use an ORDER BY command to load the reporting tables.
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Jessenia
11 months ago
I think option C is the way to go. Sorting the data in the reporting tables will definitely help with the pruning process.
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Alica
11 months ago
I think we should try creating larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.
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Mohammad
11 months ago
I agree, using an ORDER BY command will definitely help with pruning the reporting tables.
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Mitsue
11 months ago
I think we should try increasing the size of the virtual warehouse to see if that improves the pruning process.
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Wei
11 months ago
I agree, sorting the data using ORDER BY will definitely help with pruning.
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