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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?

Show Suggested Answer Hide 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

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Rodrigo
2 months ago
Wait, we can't use the 'most' efficient solution? I thought that was the whole point of the question. *scratches head*
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Arletta
19 days ago
B: B) Tokenize the data and transform the data into tabular data. Train an Amazon SageMaker k-means model to generate the product categories.
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Bernardo
20 days ago
A: A) Build 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.
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Rosamond
2 months ago
I'm not sure, option C also seems like a good choice with the Neural Topic Model. It could provide accurate product categories.
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Silvana
2 months ago
I agree with Mariann. Building a custom clustering model and using Docker image in Amazon SageMaker sounds efficient.
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Launa
2 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.
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Cecil
1 months ago
C: Maybe we should do a deeper dive into both options before making a decision.
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Hoa
1 months ago
B: I agree, but we should definitely consider the trade-offs compared to k-means.
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Jerry
1 months ago
A: Option C with the Neural Topic Model could be interesting.
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Chun
2 months ago
Option D with Blazing Text seems intriguing, but I'm not sure it's the 'most' operationally efficient choice here.
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Eun
2 months ago
I'm not a fan of the custom clustering model in Option A. Too much overhead for this use case.
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Wilbert
2 months ago
Option B looks like the way to go. Tokenizing and using SageMaker's k-means is a straightforward solution.
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Willard
1 months ago
I think it will save a lot of time and effort compared to building a custom clustering model.
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Jeannetta
1 months ago
It's definitely a practical approach to categorize the documents efficiently.
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Vincenza
2 months ago
I agree, using k-means for clustering is a good choice for this task.
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Catherin
2 months ago
Option B looks like the way to go. Tokenizing and using SageMaker's k-means is a straightforward solution.
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Mariann
2 months ago
I think option A is the best choice for operational efficiency.
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