Topic 1 Question 218

AWS Certified Machine Learning - Specialty

Topic 1 Question 218
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?
- 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.
- Tokenize the data and transform the data into tabular data. Train an Amazon SageMaker k-means model to generate the product categories.
- Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.
- Train an Amazon SageMaker Blazing Text model to generate the product categories.
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- 正解だと思う選択肢: C
  C. Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.
  
  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.
  
  👍 6
  AjoseO2023/02/20
- 正解だと思う選択肢: D
  Assign pre-defined categories to documents in a corpus: categorize books in a library into academic disciplines - BlazingText algorithm
  
  https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
  
  👍 2
  mawsman2023/04/17
- No predefined product category: topic modeling with NTM or LDA (Organize a set of documents into topics (not known in advance): tag a document as belonging to a medical category based on the terms used in the document.)
  
  Predefined product category: topic modeling with blazing text (categorize books in a library into academic disciplines)
  
  c
  
  👍 1
  drcok872023/02/10
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