A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to de

Question

Options

• AUse Amazon Rekognition to analyze sentiments of the chat conversations.
• BTrain a Naive Bayes classifier to analyze sentiments of the chat conversations.
• CUse Amazon Comprehend to analyze sentiments of the chat conversations.
• DUse random forests to classify sentiments of the chat conversations.

• A
  15% (6)
• B
  3% (1)
• C
  74% (29)
• D
  8% (3)

Explanation

Amazon Comprehend is a purpose-built NLP service that performs sentiment analysis on text out of the box - no model training, no data labeling, no infrastructure setup required, making it the fastest path to analyzing chat logs. Amazon Rekognition (A) is an image and video analysis service with no text sentiment capability, so it's simply the wrong tool for text data. Options B and D (Naive Bayes and Random Forests) are valid ML approaches for sentiment classification, but they require collecting labeled training data, feature engineering, training, and evaluation cycles - all of which take significant time compared to calling a managed API. The key phrase in the question is "least amount of time", which always points toward a fully managed AWS AI service over a custom-trained model.

Memory tip: When an AWS exam question mentions text + sentiment/entities/language + fastest/least effort, think Comprehend. When it mentions images/video + faces/objects, think Rekognition. The service names are hints - Comprehend understands text; Rekognition recognizes visuals.

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