Exam AWS Certified Machine Learning - Specialty topic 1 question 182 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 182
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A data scientist is reviewing customer comments about a company's products. The data scientist needs to present an initial exploratory analysis by using charts and a word cloud. The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model.
Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)

A. Named entity recognition
B. Coreference
C. Stemming
D. Term frequency-inverse document frequency (TF-IDF)
E. Sentiment analysis

Show Suggested Answer

Suggested Answer: CD 🗳️

by ckkobe24 at May 2, 2022, 3:45 a.m.

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DJiang

Highly Voted 3 years, 1 month ago

Selected Answer: CD

Sentiment analysis is the result of analysis, not feature engineering. I think this answer should be C & D.

upvoted 12 times

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hk0308

Most Recent 6 months, 3 weeks ago

Selected Answer: C

D is also wrong. For creating a word cloud, the frequency of words (not their inverse frequency, as used in TF-IDF) is typically the most appropriate metric. Word clouds are designed to visually represent how often a word appears in the text, with more frequent words appearing larger and more prominent.

upvoted 1 times

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loict

1 year, 9 months ago

Selected Answer: CD

A. NO - it is categorization of words and thus inferencing, not pre-processing B. NO - Coreferencing (eg. linking "He" to "Mark" seen in a previous sentence) is a complex task, not pre-processing C. YES - it consists of reducing words to their base form to reduce dimensionality D. YES - fast pre-processing task E. NO - it is not feature engineering, it is training

upvoted 3 times

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