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MLS-C01 · Question #312
MLS-C01 Question #312: Real Exam Question with Answer & Explanation
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Modeling
Question
A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams. A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified. Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?
Options
- AUse the SageMaker Latent Dirichlet Allocation (LDA) algorithm.
- BUse the SageMaker BlazingText algorithm.
- CUse the SageMaker Neural Topic Model (NTM) algorithm.
- DUse the SageMaker CatBoost algorithm.
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Topics
#SageMaker Algorithms#Text Classification#Multi-class Classification#Supervised Learning