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Snowflake Exam ARA-C01 Topic 2 Question 33 Discussion

Actual exam question for Snowflake's ARA-C01 exam
Question #: 33
Topic #: 2
[All ARA-C01 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.


* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1

by Haydee at Aug 08, 2024, 04:18 PM

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Van
2 months ago
I think we should consider option B) Increase the size of the virtual warehouse to a size 5X-Large. It might help with the performance issues.
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Shannon
2 months ago
I agree with Isadora, option C) seems like a good solution to improve pruning of the reporting tables.
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Alesia
2 months ago
I disagree, I believe option D) Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute would be more effective.
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Isadora
2 months ago
I think we should try option C) Use an ORDER BY command to load the reporting tables. It might help with pruning.
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Veronique
3 months ago
I'm not sure about those options. Maybe we should consider option B) Increase the size of the virtual warehouse to a size 5X-Large for better performance.
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Latonia
3 months ago
D is an interesting idea, but I'm not sure larger files and faster ingestion would solve the pruning issue. C seems like the most targeted approach.
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Lou
2 months ago
It might help with performance, but C seems more focused on the pruning issue.
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Clay
2 months ago
But wouldn't increasing the size of the virtual warehouse also help?
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Kiley
2 months ago
Agreed, using an ORDER BY command could help.
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Eden
2 months ago
I think C is the best option to improve pruning.
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Rodolfo
3 months ago
I disagree, I believe option D) Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute would be more effective.
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Shoshana
3 months ago
I think we should try option C) Use an ORDER BY command to load the reporting tables. It might help with pruning.
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Micah
3 months ago
Haha, eliminating Snowpipe and using PUT commands (A) sounds like a step backwards. Let's stick with the automated ingestion.
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Lorriane
3 months ago
Increasing the virtual warehouse size to 5X-Large (B) seems like overkill. I'd try the ORDER BY solution first before resorting to that.
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Gerardo
3 months ago
I think the correct answer is C. Using an ORDER BY clause on the cluster key(s) should help with the pruning of the reporting tables.
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Terrilyn
3 months ago
Yeah, I think using an ORDER BY clause on the cluster key(s) would definitely optimize the pruning process.
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Theola
3 months ago
I agree, that sounds like the right approach to improve the pruning of the reporting tables.
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Kenia
3 months ago
I think the correct answer is C. Using an ORDER BY clause on the cluster key(s) should help with the pruning of the reporting tables.
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