Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25

- Free Preparation Discussions

Amazon Exam MLS-C01 Topic 3 Question 110 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 110
Topic #: 3

[All MLS-C01 Questions]

A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products.

Which solution will meet these requirements with the MOST operational efficiency?

ABuild a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.

BTokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.

CTrain an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

DTrain an Amazon SageMaker Blazing Text model to generate the product categories.

Show Suggested Answer

Suggested Answer: C

Amazon SageMaker's Neural Topic Model (NTM) is designed to uncover underlying topics within text data by clustering documents based on topic similarity. For document categorization, NTM can identify product categories by analyzing and grouping the documents, making it an efficient choice for unsupervised learning where predefined categories do not exist.

by Dana at Jan 13, 2025, 07:50 AM

Limited Time Offer

25%

Off

Get Premium MLS-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Rodrigo

19 days ago

Wait, we can't use the 'most' efficient solution? I thought that was the whole point of the question. *scratches head*

upvoted 0 times

...

Rosamond

21 days ago

I'm not sure, option C also seems like a good choice with the Neural Topic Model. It could provide accurate product categories.

upvoted 0 times

...

Silvana

23 days ago

I agree with Mariann. Building a custom clustering model and using Docker image in Amazon SageMaker sounds efficient.

upvoted 0 times

...

Launa

1 months ago

Option C with the Neural Topic Model could be interesting, but I'd want to understand the trade-offs compared to k-means.

upvoted 0 times

Cecil

5 days ago

C: Maybe we should do a deeper dive into both options before making a decision.

upvoted 0 times

...

Hoa

8 days ago

B: I agree, but we should definitely consider the trade-offs compared to k-means.

upvoted 0 times

...

Jerry

12 days ago

A: Option C with the Neural Topic Model could be interesting.

upvoted 0 times

...

...

Chun

1 months ago

Option D with Blazing Text seems intriguing, but I'm not sure it's the 'most' operationally efficient choice here.

upvoted 0 times

...

Eun

1 months ago

I'm not a fan of the custom clustering model in Option A. Too much overhead for this use case.

upvoted 0 times

...

Wilbert

1 months ago

Option B looks like the way to go. Tokenizing and using SageMaker's k-means is a straightforward solution.

upvoted 0 times

Willard

5 days ago

I think it will save a lot of time and effort compared to building a custom clustering model.

upvoted 0 times

...

Jeannetta

9 days ago

It's definitely a practical approach to categorize the documents efficiently.

upvoted 0 times

...

Vincenza

22 days ago

I agree, using k-means for clustering is a good choice for this task.

upvoted 0 times

...

Catherin

28 days ago

Option B looks like the way to go. Tokenizing and using SageMaker's k-means is a straightforward solution.

upvoted 0 times

...

...

Mariann

1 months ago

I think option A is the best choice for operational efficiency.

upvoted 0 times

...