Snowflake ARA-R01 Exam - Topic 4 Question 1 Discussion

Actual exam question for Snowflake's ARA-R01 exam

Question #: 1
Topic #: 4

A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.

Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the dat

a. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer.

What solution will MINIMIZE complexity and MAXIMIZE performance?

A1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
4. Give the transformed table access to the dashboard tool.
5. Perform the aggregations on the dashboard tool.

B1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
4. Join the vendor's data back to the loT data using a transformation procedure.
5. Create views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the views access to the dashboard tool.

C1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
5. Give the materialized views access to the dashboard tool.

D1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
4. Join the vendor's data back to the loT data in a transformation procedure
5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the materialized views access to the dashboard tool.

Show Suggested Answer

Suggested Answer: D

Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.

Reference =

* Snowflake Documentation on Snowpipe4

* Snowflake Documentation on Secure Data Sharing2

* Best Practices for Data Ingestion with Snowflake1

by Viva at Apr 24, 2024, 08:40 AM

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Theola

3 months ago

External tables are a must for handling JSON data efficiently!

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Milly

3 months ago

I disagree, D has the best performance with materialized views.

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Gary

3 months ago

Surprised that no one mentioned using materialized views in option A!

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Cassi

4 months ago

I think C is better for real-time processing with Snowpipe.

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Marge

4 months ago

Option B seems solid with the data share approach.

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Evangelina

4 months ago

I’m leaning towards option D because it combines Snowpipe and data sharing, but I’m a bit uncertain about how materialized views work in this context.

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Quentin

4 months ago

I practiced a similar question, and I feel like using streams and tasks is crucial for handling the frequent updates, but I can't recall the exact details.

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Kanisha

4 months ago

I think option B sounds familiar; it mentions creating a data share with the vendor, which could simplify the process.

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Cheryl

5 months ago

I remember we discussed using Snowpipe for real-time data ingestion, but I'm not sure if it's the best option here.

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Alonso

5 months ago

Whoa, this is a lot to take in. All these different options with multiple steps, I'm not sure which one is the best approach. I better re-read the question and really focus on the requirements of minimizing complexity and maximizing performance.

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James

5 months ago

Okay, I think I've got a good strategy here. The key is to use Snowpipe and streams/tasks to automate the data ingestion and transformation as much as possible. And getting the vendor data through a data share seems like the simplest way to integrate that. I feel pretty confident about this one.

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Carri

5 months ago

Hmm, I'm a bit confused about the different options here. They all seem to involve a lot of steps, and I'm not sure which one would really minimize complexity the most. I'll need to think this through carefully.

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Anika

5 months ago

This seems like a pretty straightforward question, I think I can handle it. The key is to focus on minimizing complexity and maximizing performance, just like the question asks.

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Ilda

5 months ago

Hmm, I'm not sure about this one. The Reports and Dashboards module might give me some insights into the customer data, but I'm not confident that's the right place to start.

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Isaac

5 months ago

I'm a bit confused by the different role options here. I'll need to review the material again before answering.

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Virgina

5 months ago

I think the key here is to identify the minimum number of test conditions needed to fulfill the exit criteria. The question mentions EXCR1 and EXCR2, so I'll need to review those carefully.

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Alida

5 months ago

Okay, I think I've got this. I just need to multiply the net present value of each outcome by the probability of that outcome occurring, and then sum them up to get the expected value for each project.

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