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Snowflake Exam ARA-C01 Topic 2 Question 29 Discussion

Actual exam question for Snowflake's ARA-C01 exam
Question #: 29
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 Glory at Jun 24, 2024, 10:10 PM

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Melvin
4 months ago
That's a good point, Lanie. Maybe we can try both options C and D to see which one works better.
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Lanie
4 months ago
I'm not sure about option C, maybe we should also consider option D) Create larger files for Snowpipe to ingest.
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Noel
4 months ago
I agree with Melvin, that sounds like a good solution to improve pruning.
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Melvin
4 months ago
I think we should try option C) Use an ORDER BY command to load the reporting tables.
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Latanya
5 months ago
Hmm, I'm torn between C) and D). Ordering the data could help, but maybe they should also look into Snowpipe performance. Bigger files and less frequent ingestion might be the way to go. Either way, I hope they're not using a stone-age virtual warehouse!
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Glenn
5 months ago
B) is the way to go! Gotta go big or go home. More power to the warehouse, more power to the people! Why optimize when you can just brute force it, am I right?
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Mel
3 months ago
B) Yeah, bigger is always better when it comes to virtual warehouses!
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Kenda
4 months ago
B) Increase the size of the virtual warehouse to a size 5X-Large.
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Ligia
5 months ago
I'm going to go with D). Bigger files and less frequent ingestion should help optimize the pipeline. Though I do wonder if the engineers considered getting a bigger virtual warehouse...
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Leonora
4 months ago
A) Eliminate the use of Snowpipe and load the files into internal stages using PUT commands.
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Kattie
4 months ago
D) Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.
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Kris
5 months ago
C) seems like the obvious choice here. Ordering the data before loading it into the reporting tables should help with pruning. The other options don't seem directly related to the issue at hand.
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Vilma
4 months ago
Let's implement the ORDER BY command and monitor the results.
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Rasheeda
4 months ago
I think we should give it a try and see if it improves the situation.
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Gertude
4 months ago
I agree, using an ORDER BY command should help with pruning.
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