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]

What Snowflake system functions are used to view and or monitor the clustering metadata for a table? (Select TWO).

ASYSTEMSCLUSTERING

BSYSTEMSTABLE_CLUSTERING

CSYSTEMSCLUSTERING_DEPTH

DSYSTEMSCLUSTERING_RATIO

ESYSTEMSCLUSTERING_INFORMATION

Show Suggested 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

6 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

6 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

6 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

7 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

7 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

7 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

6 months ago

It might help with performance, but C seems more focused on the pruning issue.

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Clay

6 months ago

But wouldn't increasing the size of the virtual warehouse also help?

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Kiley

6 months ago

Agreed, using an ORDER BY command could help.

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Eden

7 months ago

I think C is the best option to improve pruning.

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Rodolfo

7 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

7 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

8 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

8 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

8 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

7 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

7 months ago

I agree, that sounds like the right approach to improve the pruning of the reporting tables.

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Kenia

7 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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