AI Practitioner · 14% of the exam

Guidelines for Responsible AI: free practice questions

5 sample questions from our 10-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real AWS exam.

1. What is the purpose of EXPLAINABILITY in responsible AI?

  • A. To help humans understand WHY a model produced a given output or decision✓ Correct
  • B. To make the model run faster
  • C. To reduce the token cost of inference
  • D. To encrypt the training data
Explanation

Explainability provides insight into why a model reached a decision, supporting trust, debugging, and compliance. It is not about speed (B), cost (C), or encryption (D), which are performance and security concerns.

2. Which statement about responsible AI and human oversight is MOST accurate?

  • A. For high-stakes decisions, keeping humans in the loop to review AI outputs is a recommended responsible-AI practice✓ Correct
  • B. AI systems should always operate fully autonomously in high-stakes settings
  • C. Human review always eliminates the need for any model evaluation
  • D. Responsible AI only concerns model accuracy
Explanation

Human-in-the-loop review of AI outputs is a recommended safeguard for high-stakes decisions. Full autonomy in high-stakes settings (B) is risky, human review doesn't replace evaluation (C), and responsible AI spans fairness, transparency, safety, and more — not accuracy alone (D).

3. Which document provides standardized information about a model's intended use, performance, limitations, and considerations to promote transparency?

  • A. A model card✓ Correct
  • B. An IAM policy
  • C. A VPC route table
  • D. A billing invoice
Explanation

A model card documents a model's intended use, performance characteristics, limitations, and ethical considerations — a transparency artifact (Amazon SageMaker offers Model Cards). IAM policies (B), route tables (C), and invoices (D) serve security, networking, and billing purposes.

4. Which of the following are recognized DIMENSIONS of responsible AI? (Select 2)

  • A. Fairness and bias mitigation✓ Correct
  • B. Transparency and explainability✓ Correct
  • C. Maximum GPU count
  • D. Number of S3 buckets
  • E. CDN cache hit ratio
Explanation

Fairness/bias mitigation and transparency/explainability are core responsible-AI dimensions (alongside safety, robustness, privacy, and governance). GPU count (C), bucket count (D), and cache hit ratio (E) are infrastructure metrics, not responsible-AI dimensions.

5. A generative AI application must prevent users from eliciting instructions for dangerous activities. Which approach directly enforces this at the application layer on Bedrock?

  • A. Configure Bedrock Guardrails to deny those topics and filter harmful content✓ Correct
  • B. Increase the max-tokens setting
  • C. Switch the model to a different AWS Region
  • D. Enable S3 Transfer Acceleration
Explanation

Bedrock Guardrails let you define denied topics and content filters that block unsafe requests and responses. Max tokens (B), Region (C), and S3 Transfer Acceleration (D) are unrelated to content safety enforcement.

5 more questions in this domain

Practice the full bank with instant grading, flashcards, and a timed mock exam.

Start practicing free
Guidelines for Responsible AI — Free AI Practitioner Practice Questions | DataCertPrep — Certification Prep