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MLS-C01 · Question #312

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…

The correct answer is B. Use the SageMaker BlazingText algorithm. To automate the classification of free-text user feedback into predefined categories using historical labeled data, the SageMaker BlazingText algorithm is the most suitable choice.

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.

How the community answered

(14 responses)
  • A
    14% (2)
  • B
    79% (11)
  • D
    7% (1)

Why each option

To automate the classification of free-text user feedback into predefined categories using historical labeled data, the SageMaker BlazingText algorithm is the most suitable choice.

AUse the SageMaker Latent Dirichlet Allocation (LDA) algorithm.

SageMaker Latent Dirichlet Allocation (LDA) is an unsupervised algorithm used for topic modeling, which identifies abstract "topics" in a collection of documents, not for classifying text into predefined categories with labeled data.

BUse the SageMaker BlazingText algorithm.Correct

Amazon SageMaker BlazingText is a highly optimized algorithm for text classification and word embeddings. Given the need to classify free-text feedback into fixed categories with a large labeled dataset, BlazingText is an efficient and accurate choice for this supervised learning task.

CUse the SageMaker Neural Topic Model (NTM) algorithm.

SageMaker Neural Topic Model (NTM) is also an unsupervised algorithm for topic modeling, similar to LDA, and is not suitable for supervised text classification with predefined labels.

DUse the SageMaker CatBoost algorithm.

SageMaker CatBoost is a gradient boosting algorithm designed for tabular data, particularly effective with categorical features, and is not specifically optimized for free-text classification.

Concept tested: SageMaker text classification algorithms

Source: https://docs.aws.amazon.com/sagemaker/latest/dg/blazingtext.html

Topics

#SageMaker Algorithms#Text Classification#Multi-class Classification#Supervised Learning

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