MLS-C01 · Question #246
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…
The correct answer is C. Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product. The task is to build a machine learning model to categorize documents for all the company's products. Among the given options, training an Amazon SageMaker Neural Topic Model (NTM) model would be the most efficient and effective solution. An NTM model can identify topics in…
Question
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?
Options
- ABuild a custom clustering model. Create a Dockerfile and build a Docker image. Register the
- BTokenize the data and transform the data into tabular data. Train an Amazon SageMaker k-
- CTrain an Amazon SageMaker Neural Topic Model (NTM) model to generate the product
- DTrain an Amazon SageMaker Blazing Text model to generate the product categories.
How the community answered
(26 responses)- A12% (3)
- B4% (1)
- C81% (21)
- D4% (1)
Explanation
The task is to build a machine learning model to categorize documents for all the company's products. Among the given options, training an Amazon SageMaker Neural Topic Model (NTM) model would be the most efficient and effective solution. An NTM model can identify topics in text data and group similar documents into specific categories, making it a suitable model for document categorization. With an NTM model, the data scientist would not need to define product categories beforehand, as the model would automatically group similar documents into topics. This saves time and resources compared to the other options.
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