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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
2 months ago
That could also work, larger files might help with pruning efficiency.
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Sina
2 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
3 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
2 months ago
Yeah, that might be a good solution to improve performance.
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Gladys
2 months ago
That could help with the pruning issue in the reporting tables.
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Annalee
2 months ago
Maybe they should try increasing the size of the virtual warehouse to a size 5X-Large.
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Casey
3 months ago
I agree with Polly, using ORDER BY can help improve pruning.
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Denna
3 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
3 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
2 months ago
Let's give it a try and see if it improves the performance of the data pipeline.
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Rozella
2 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
2 months ago
Yes, that could definitely help with the pruning issue in the reporting tables.
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Myong
2 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
3 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
3 months ago
B: Yeah, that could definitely improve the performance without just increasing the warehouse size.
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Oliva
3 months ago
A: I think using an ORDER BY command might help with pruning the reporting tables more effectively.
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Polly
3 months ago
I think we should try option C) Use an ORDER BY command to load the reporting tables.
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Jessenia
3 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
2 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
2 months ago
I agree, using an ORDER BY command will definitely help with pruning the reporting tables.
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Mitsue
3 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
3 months ago
I agree, sorting the data using ORDER BY will definitely help with pruning.
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