Snowflake ARA-C01 Exam - Topic 2 Question 43 Discussion

Actual exam question for Snowflake's ARA-C01 exam

Question #: 43
Topic #: 2

The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:

1) Finance and Vendor Management team members who require reporting and visualization

2) Data Science team members who require access to raw data for ML model development

3) Sales team members who require engineered and protected data for data monetization

What Snowflake data modeling approaches will meet these requirements? (Choose two.)

AConsolidate data in the company's data lake and use EXTERNAL TABLES.

BCreate a raw database for landing and persisting raw data entering the data pipelines.

CCreate a set of profile-specific databases that aligns data with usage patterns.

DCreate a single star schema in a single database to support all consumers' requirements.

ECreate a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

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.

Reference =

* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1

by Della at Dec 09, 2024, 11:58 AM

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Kerry

4 months ago

C is smart! Tailoring databases for specific profiles makes sense.

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Xuan

5 months ago

Wait, can a single star schema really handle all those requirements?

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Lashonda

5 months ago

D seems too limiting for diverse needs.

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Angelo

5 months ago

Totally agree with B! Raw data is crucial for ML.

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Domingo

5 months ago

I think B and C are the best options here.

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Lilli

6 months ago

I recall that a star schema can simplify reporting, but I wonder if option D would be too limiting for the diverse needs of the teams.

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Brittni

6 months ago

I feel like consolidating data in a data lake with EXTERNAL TABLES could be useful, but I'm not confident if it meets all the requirements.

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Sabina

6 months ago

I'm not entirely sure, but I think creating profile-specific databases like in option C might help tailor the data for different teams. That sounds familiar from our practice questions.

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Filiberto

6 months ago

I remember we discussed the importance of having a raw database for landing data. It seems like option B could be a good fit for the Data Science team.

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Salena

6 months ago

I feel pretty confident about this one. Creating profile-specific databases and using a raw database for landing data seem like the best approaches to handle the varied requirements. I'll select those two options.

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Mertie

6 months ago

Okay, I think I've got a strategy here. The key is to identify the approaches that provide the right balance of data consolidation, data access, and data protection. I'll weigh the pros and cons of each option.

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Myra

6 months ago

Hmm, I'm a bit confused by the options. I'm not sure which two would best meet the needs of the finance, data science, and sales teams. I'll have to review the details more closely.

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Tu

6 months ago

This looks like a tricky question. I'll need to think carefully about the different data modeling approaches and how they align with the requirements.

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Socorro

11 months ago

Ah, the age-old debate: centralize everything or distribute by use case? I'm leaning towards C and B - keep the raw data separate, but build out those profile-specific databases to make everyone's lives easier.

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Florinda

11 months ago

E is definitely the most elegant solution, but I'm not sure the sales team is going to be thrilled about having to go through the Vault for their data monetization needs. C and B seem like they strike a better balance.

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Cherelle

11 months ago

Haha, I bet the finance team is going to love having to access the raw data in the Data Vault! 'Sorry, can't give you that report, you'll have to dig through the Vault.'

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Lawanda

10 months ago

A: True, but the finance team might not be too happy about having to dig through the Data Vault for their reports.

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An

10 months ago

B: Yeah, but wouldn't it be easier to just consolidate data in the data lake and use external tables for reporting?

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Blair

10 months ago

A: I think option C would be a good approach to align data with specific usage patterns.

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Nada

10 months ago

C) Create a set of profile-specific databases that aligns data with usage patterns.

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Gaston

11 months ago

B) Create a raw database for landing and persisting raw data entering the data pipelines.

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Willodean

11 months ago

A) Consolidate data in the company's data lake and use EXTERNAL TABLES.

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Rosann

11 months ago

D is a tempting choice, but I think that would be too rigid and difficult to manage in the long run. C and B seem like the best balance between flexibility and data governance.

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Brigette

10 months ago

Having profile-specific databases aligned with usage patterns sounds like a good approach.

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Julian

10 months ago

I think C and B would provide the flexibility we need while still maintaining data governance.

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Susy

11 months ago

I agree, D might be too rigid for our diverse data consumer requirements.

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Louis

12 months ago

C and E seem like the most viable options here. Separating the data by usage patterns and having a centralized Data Vault make a lot of sense for this scenario.

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