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Snowflake Exam ARA-R01 Topic 2 Question 26 Discussion

Actual exam question for Snowflake's ARA-R01 exam
Question #: 26
Topic #: 2
[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 Brent at Jan 11, 2025, 10:17 AM

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Elfrieda
2 months ago
I'm not sure about option C, maybe we should also look into option D to create larger files for Snowpipe.
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Deandrea
2 months ago
Wait, they're using a 4X-Large warehouse to query *reporting* tables? Talk about going big or going home! I bet they're keeping the local power company in business.
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Precious
2 months ago
I agree with Alita, using an ORDER BY command could help optimize the pruning process.
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Wendell
2 months ago
Ah, the old 'eliminate Snowpipe and use manual PUT commands' trick, eh? That's a bold move, Cotton. Let's see if it pays off.
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Annalee
1 months ago
B: Yeah, that could help improve the pruning of the reporting tables.
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Elvis
1 months ago
A: I think we should try using an ORDER BY command to load the reporting tables.
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Serita
2 months ago
D is the way to go, no doubt. Bigger files and faster ingestion? That's the key to a well-oiled data pipeline. Plus, it's like a digital version of the 'supersizing' we all know and love.
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Vonda
2 months ago
I dunno, B seems like the easiest fix. Just crank up the warehouse size and call it a day, right? Who needs fancy optimization when you can just throw more resources at it?
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Ty
1 months ago
I think C might actually be a better solution. Ordering the data before loading could help with pruning efficiency.
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Rasheeda
1 months ago
C) Use an ORDER BY command to load the reporting tables.
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Shawnda
1 months ago
B) Increase the size of the virtual warehouse to a size 5X-Large.
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Letha
2 months ago
Hmm, I think the answer is C. Using an ORDER BY command to load the reporting tables should help with the pruning issue. That's a pretty straightforward solution.
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Meghan
1 months ago
Yeah, combining both solutions could be the best approach to improve the pruning of the reporting tables.
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Salina
1 months ago
That's true, larger files could help too. Maybe a combination of both solutions would be even more effective.
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Rikki
2 months ago
But wouldn't creating larger files for Snowpipe to ingest also improve the pruning?
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Sarina
2 months ago
I agree, using an ORDER BY command can definitely help with the pruning issue.
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Alita
2 months ago
I think we should consider option C to improve pruning of the reporting tables.
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