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Question 305 - MLS-C01 discussion

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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?

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.

Answers
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.

B.

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

Answers
B.

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

C.

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

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C.

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

D.

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

Answers
D.

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

Suggested answer: C

Explanation:

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.

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asked 31/10/2024
Christopher Harden
46 questions
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