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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 68 discussion

An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
  • B. Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
  • C. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
  • D. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.
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Suggested Answer: D 🗳️

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Saransundar
1 month ago
Selected Answer: D
Key phrase extraction and custom entity recognition - Amazon Comprehend helps with least operational overhead.
upvoted 2 times
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GiorgioGss
1 month, 1 week ago
Selected Answer: D
https://docs.aws.amazon.com/comprehend/latest/dg/what-is.html
upvoted 1 times
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