Questão 47 — Amazon AIF-C01 Simulado | AWS Certified AI Practitioner Questões e Respostas
Which technique can a company use to lower bias and toxicity in generative AI applications during the post- processing ML lifecycle?
- A. Human-in-the-loop
- B. Data augmentation
- C. Feature engineering
- D. Adversarial training
Resposta correta:
A
Explicação
Explanation: The correct answer is A because Human-in-the-loop (HITL) is a post-processing strategy used to monitor, review, and filter outputs from generative AI models for toxicity, bias, or inappropriate content. It allows human reviewers to approve or reject model responses before they are delivered to end-users, ensuring alignment with ethical guidelines and company policies. From the AWS documentation: "Human-in-the-loop (HITL) workflows in generative AI are used to validate and approve outputs of models, especially in applications where content quality, compliance, or harm reduction is critical. HITL is a key step in responsible AI implementations to mitigate hallucinations, bias, and unsafe content." Explanation of other options: B . Data augmentation is a pre-processing technique to increase data diversity, not typically used in post- processing stages. C . Feature engineering is relevant in traditional ML, especially structured data tasks, not typically used in generative AI post-processing. D . Adversarial training is a model training strategy, not a post-processing mitigation approach. Referenced AWS AI/ML Documents and Study Guides: AWS Responsible AI Practices Whitepaper AWS Generative AI Developer Guide Human-in-the-loop and Post-processing Amazon A2I Documentation Integrating Human Review in ML Workflows