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Snowflake Exam ARA-C01 Topic 2 Question 43 Discussion

Actual exam question for Snowflake's ARA-C01 exam
Question #: 43
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.

Reference =

* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1


Contribute your Thoughts:

Providencia
2 months ago
I wonder if the engineers at this company have tried turning it off and on again. Just kidding, but C does seem like the most logical choice here.
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Reuben
3 months ago
Snowpipe is pretty awesome, so I wouldn't want to eliminate it entirely like in A. I agree with C, ordering the data is the way to go.
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Mari
2 months ago
Using ORDER BY will definitely improve the efficiency of the pruning process.
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Georgiann
2 months ago
Yeah, I agree. It's important to optimize the way the data is loaded into the reporting tables.
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Melissa
2 months ago
I think C is the best option, ordering the data will definitely help with pruning.
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Nelida
3 months ago
Hmm, I'm not sure if B is the best option. Throwing more compute power at the problem might not be the most efficient solution. C seems like the way to go.
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Stephen
3 months ago
D sounds interesting, but I'm not sure if increasing the file size and staging frequency will really address the pruning issue. I'm leaning towards C.
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Paulina
2 months ago
Let's give it a try and see if it improves the performance of the reporting tables.
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Weldon
2 months ago
I agree, using an ORDER BY command could optimize the pruning process.
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Cherelle
2 months ago
I think C could help with the pruning issue by organizing the data in the reporting tables.
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Jamal
3 months ago
That's a valid point, Marylou. It's worth considering both options C and D for improving the pruning of the reporting tables.
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Marylou
3 months ago
But what about option D? Creating larger files for Snowpipe to ingest might also be a good solution.
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Chanel
3 months ago
I agree with Jamal, using an ORDER BY command could help optimize the pruning process.
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Jamal
3 months ago
I think we should consider option C to improve pruning of the reporting tables.
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Valentine
3 months ago
I think C is the correct answer. Ordering the data in the reporting tables will help with pruning and improve query performance.
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Amie
3 months ago
Makes sense, let's go with option C then.
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Odette
3 months ago
It might, but I think ordering the data first is a more direct solution to the pruning issue.
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Rikki
3 months ago
But wouldn't increasing the size of the virtual warehouse also improve query performance?
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Daren
3 months ago
I agree, using an ORDER BY command will definitely help with pruning.
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