AIF-C01 Guidelines for Responsible AI Practice Question
A financial services company is deploying a generative AI chatbot to assist customers with account inquiries. The company wants to ensure the chatbot does not generate biased or harmful responses. Which combination of AWS services and practices should the company implement to monitor and mitigate these risks?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between services that detect customer sentiment (like Amazon Comprehend or Lex sentiment analysis) versus services that detect bias in model outputs (like SageMaker Clarify), leading candidates to mistakenly choose sentiment analysis options for bias detection.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker Clarify to detect bias in model outputs and implement a human-in-the-loop workflow with Amazon A2I to review flagged responses.
Amazon SageMaker Clarify is specifically designed to detect bias in machine learning models and their outputs, while Amazon Augmented AI (A2I) enables a human-in-the-loop workflow to review flagged responses. This combination directly addresses the requirement to monitor and mitigate biased or harmful responses from a generative AI chatbot, ensuring responsible AI practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the chatbot to use a pre-trained model from SageMaker JumpStart and disable logging to avoid storing sensitive customer data.
Why it's wrong here
Disabling logging removes the ability to audit and monitor for harmful outputs.
- ✗
Use Amazon Rekognition to analyze chat logs for biased language and automatically block responses with a confidence score above 90%.
Why it's wrong here
Amazon Rekognition is designed for image and video analysis, not text-based bias detection.
- ✓
Use Amazon SageMaker Clarify to detect bias in model outputs and implement a human-in-the-loop workflow with Amazon A2I to review flagged responses.
Why this is correct
SageMaker Clarify provides bias detection, and Amazon A2I enables human review of model outputs to ensure responsible AI.
- ✗
Deploy Amazon Lex with built-in sentiment analysis to detect negative customer emotions and automatically escalate to a human agent.
Why it's wrong here
Amazon Lex does not have built-in bias detection for generative AI responses.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.