AWS Certified Cloud Practitioner

Explanation

Correct Answer: B - Use Amazon Comprehend toxicity detection.

Why this is correct:

1. Amazon Comprehend is Amazon's natural language processing (NLP) service that includes a Toxicity Detection feature specifically designed to identify harmful language, hate speech, and toxic content.
2. No labeled data required: Amazon Comprehend's toxicity detection is a pre-trained model that works out-of-the-box without requiring any labeled training data from the user.
3. Perfect fit for the use case: The service is specifically designed for analyzing text content like social media comments to identify harmful language.

Why other options are incorrect:

A. Use Amazon Rekognition moderation.

• Amazon Rekognition is primarily for image and video analysis, not text analysis.
• While it has content moderation features, they are focused on visual content (inappropriate images/videos), not text comments.

C. Use Amazon SageMaker built-in algorithms to train the model.

• This would require labeled data to train the model, which contradicts the requirement that "the company will not use labeled data to train the model."
• SageMaker is for building custom ML models, which typically require training data.

D. Use Amazon Polly to monitor comments.

• Amazon Polly is a text-to-speech service that converts text into lifelike speech.
• It has no capability for content analysis, toxicity detection, or harmful language identification.

Key AWS Service Distinctions:

• Amazon Comprehend: NLP service for text analysis (sentiment, entities, key phrases, toxicity)
• Amazon Rekognition: Computer vision service for image/video analysis
• Amazon SageMaker: ML platform for building, training, and deploying custom models
• Amazon Polly: Text-to-speech service

The requirement for "no labeled data" makes Amazon Comprehend's pre-trained toxicity detection the ideal solution.