Snowflake ARA-R01 Exam - Topic 4 Question 33 Discussion

Actual exam question for Snowflake's ARA-R01 exam

Question #: 33
Topic #: 4

A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.

The data pipeline needs to run continuously and efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.

Which design will meet these requirements?

AIngest the data using copy into and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

BIngest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

CIngest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.

DIngest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Show Suggested Answer

Suggested Answer: B

Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications.Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data.Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table.An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.

Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.

Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

by Felix at Jun 10, 2025, 10:53 PM

Limited Time Offer

25%

Off

Get Premium ARA-R01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Mari

5 months ago

C looks interesting, but isn't it overkill for this task?

upvoted 0 times

...

Kristine

6 months ago

Wait, can Snowpipe really handle all that? Sounds complicated.

upvoted 0 times

...

Salena

6 months ago

I agree, B is definitely the way to go!

upvoted 0 times

...

Misty

6 months ago

D is solid too, but I think B has the edge for simplicity.

upvoted 0 times

...

Shawnda

6 months ago

B seems like the most efficient option with Snowpipe.

upvoted 0 times

...

Cordelia

6 months ago

I lean towards option D because it also uses Snowpipe, but I wonder if exporting to S3 adds unnecessary steps. We should have discussed this more in our study group.

upvoted 0 times

...

Nickole

7 months ago

I’m a bit confused about whether we should export to S3 for Comprehend or keep everything in Snowflake. I feel like I need to review the differences again.

upvoted 0 times

...

Anna

7 months ago

I think option B is the best choice since it minimizes operational complexity and uses Snowflake's features effectively. It reminds me of a similar practice question we did on data ingestion.

upvoted 0 times

...

Breana

7 months ago

I remember we discussed Snowpipe in class, and it seems like a good fit for continuous ingestion. But I'm not entirely sure about the external function part.

upvoted 0 times

...

Jacinta

7 months ago

This seems like a pretty straightforward data engineering problem. I'm leaning towards option D - using Snowpipe for the initial data ingestion, then leveraging Snowflake's built-in capabilities for the transformations and making the data available through the Snowflake Marketplace. That should meet all the requirements while keeping the operational overhead low.

upvoted 0 times

...

Tanesha

7 months ago

Okay, let's see here. The company needs a data pipeline that can ingest customer review data, apply transformations, and then make the final data set available publicly. They also want to minimize operational complexity and development effort. I think option B might be the way to go, but I'll need to double-check the details.

upvoted 0 times

...

Judy

8 months ago

Hmm, this is a tricky one. There are a few different approaches we could take, and I'm not sure which one would be the best fit. I'll need to carefully read through the requirements and think through the pros and cons of each option.

upvoted 0 times

...

Amber

8 months ago

This looks like a pretty straightforward data pipeline design question. I think I can tackle this one - the key is to identify the most efficient and low-maintenance solution that meets all the requirements.

upvoted 0 times

...

Cherry

10 months ago

Wait, did they say the data needs to be 'de-identified'? I hope they're not trying to sell people's personal information. That would be a data privacy nightmare!

upvoted 0 times

Theodora

10 months ago

B: Yeah, they did. But I'm also concerned about the de-identification of the data.

upvoted 0 times

...

Mila

10 months ago

A: I think they mentioned using Amazon Comprehend for sentiment analysis.

upvoted 0 times

...

Paola

10 months ago

Option C looks a bit overcomplicated to me. Using Amazon EMR and PySpark seems like overkill when Snowflake has such robust data engineering capabilities built-in.

upvoted 0 times

Yong

10 months ago

I think option B might be a better choice, using Snowpipe and Amazon Comprehend directly.

upvoted 0 times

...

Tammara

10 months ago

I agree, option C does seem like it's adding unnecessary complexity.

upvoted 0 times

...

Justine

11 months ago

Ha! Good point. I bet the 'advertising companies' are just salivating at the prospect of getting their hands on all that juicy customer data. Hey, at least Snowflake Marketplace has some level of governance, right?

upvoted 0 times

Eugene

9 months ago

A: D) Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

upvoted 0 times

...

Ming

10 months ago

B: Ha! Yeah, those advertising companies must be eager to get their hands on that data. Snowflake Marketplace does provide some level of governance, which is good.

upvoted 0 times

...

Tammi

10 months ago

A: B) Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

upvoted 0 times

...

Dalene

11 months ago

That's a good point, but option C also seems viable with Amazon EMR and PySpark for transformations.

upvoted 0 times

...

Jacquelyne

11 months ago

I disagree, I believe option B is more efficient as it uses Snowpipe for ingestion and external functions for model inference.

upvoted 0 times

...

Tanesha

11 months ago

Option B sounds like the most efficient and streamlined approach. Leveraging Snowpipe and Snowflake's native capabilities to orchestrate the transformations and integrate with Amazon Comprehend seems like a great way to minimize operational complexity.

upvoted 0 times