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Exam AWS Certified Machine Learning - Specialty topic 1 question 165 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 165
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?

  • A. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
  • B. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
  • C. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
  • D. Remove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
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Suggested Answer: D 🗳️
by exam_prep at June 2, 2022, 12:20 a.m.
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exam_prep
Highly Voted 1 year, 11 months ago
D: Option A, B & C don't make sense. D removes the stop words and help in count vectors
upvoted 16 times
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kaike_reis
Most Recent 8 months, 4 weeks ago
Selected Answer: D
D The only solution that solves our problem: remove stopwords ASAP
upvoted 1 times
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Mickey321
9 months, 1 week ago
Selected Answer: D
D: Option A, B & C don't make sense. D removes the stop words and help in count vectors
upvoted 1 times
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ADVIT
10 months ago
D, ChatGPT confirm :)
upvoted 2 times
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Valcilio
1 year, 1 month ago
Selected Answer: D
Needs to remove stopwords and the rare worlds are feasible.
upvoted 3 times
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Peeking
1 year, 4 months ago
Selected Answer: D
The stop words need to be removed. The rare words don't need to be removed because it has been found that they are feasible tags.
upvoted 4 times
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HerbertK
1 year, 7 months ago
Selected Answer: A
Why not A?
upvoted 1 times
VinceCar
1 year, 5 months ago
check the requirement in question: "the generated model do not include the stopwords"
upvoted 1 times
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