Selected AIF-C01 AWS Certified AI Practitioner Questions Answers

The verified answer is C. Incorporating continuous human feedback across model development, training, and deployment phases and using performance evaluation to improve model accuracy. The question asks for human-in-the-loop improvement throughout the ML lifecycle, not a one-time review and not fully automated correction. AWS AI Practitioner guidance describes the foundation model lifecycle as including data selection, model selection, pre-training, fine-tuning, evaluation, deployment, and feedback. Because feedback is part of the lifecycle, human review should not be limited to the first training stage only.

AWS generative AI lifecycle guidance also emphasizes continuous improvement and feedback loops. It explains that operationalizing a generative AI application is not a one-time event, but an ongoing process driven by real-world usage, feedback systems, automated feedback loops, direct user feedback mechanisms, and strategic human-in-the-loop interventions. This supports option C because it combines human feedback across phases with performance evaluation to improve model behavior and accuracy over time.

Option A is incorrect because it removes human intervention. Automated testing is valuable, but it is not human-in-the-loop. Option B is incorrect because collecting human feedback only during initial training is too narrow. It ignores post-training evaluation, deployment monitoring, user feedback, drift, and iterative improvement. Option D is incorrect because it relies on automated reinforcement learning and predefined metrics without human oversight. That again contradicts the “human-in-the-loop” requirement.

The strongest clue is the phrase “throughout the ML lifecycle.” A correct human-in-the-loop strategy must include human review, human feedback, and evaluation during development, training, deployment, and ongoing improvement. Therefore, option C is the only answer that satisfies both parts of the requirement: continuous human involvement and lifecycle-based model improvement.

The correct answer is A because adversarial prompting is a defensive technique used to identify and protect against prompt injection attacks in large language models (LLMs). In adversarial prompting, developers intentionally test the model with manipulated or malicious prompts to evaluate how it behaves under attack and to harden the system by refining prompts, filters, and validation logic.

From AWS documentation:

" Adversarial prompting is used to evaluate and defend generative AI models against harmful or manipulative inputs (prompt injections). By testing with adversarial examples, developers can identify vulnerabilities and apply safeguards such as Guardrails or context filtering to prevent model misuse. "

Prompt injection occurs when an attacker tries to override system or developer instructions within a prompt, leading the model to disclose restricted information or behave undesirably. Adversarial prompting helps uncover and mitigate these risks before deployment.

Explanation of other options:

B. Zero-shot prompting provides no examples and does not protect against injection attacks.

C. Least-to-most prompting is a reasoning technique used to break down complex problems step-by-step, not a security measure.

D. Chain-of-thought prompting encourages detailed reasoning by the model but can actually increase exposure to prompt injection if not properly constrained.

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Practices – Prompt Injection and Safety Testing

Amazon Bedrock Developer Guide – Secure Prompt Design and Evaluation

AWS Generative AI Security Whitepaper – Adversarial Testing and Guardrails

The verified answer is A. AWS Audit Manager. The requirement is recurring compliance assessments with documented evidence mapped to regulatory frameworks. AWS documentation states that AWS Audit Manager helps customers continually audit AWS usage to simplify risk and compliance management. It automates evidence collection so customers can assess whether policies, procedures, and controls are operating effectively. AWS also states that Audit Manager provides prebuilt frameworks that structure and automate assessments for compliance standards and regulations.

The key phrase is “documented evidence mapped to regulatory frameworks.” AWS Audit Manager creates assessments from frameworks, automatically runs resource assessments, collects data from in-scope AWS accounts, transforms that data into audit-friendly evidence, and attaches it to relevant controls. That is the exact service function being tested. AWS also explains that Audit Manager can generate reports for auditors and supports prebuilt frameworks for standards such as PCI DSS, GDPR, HIPAA, SOC 2, and AWS operational best practices.

AWS Trusted Advisor is incorrect because Trusted Advisor provides recommendations for cost optimization, performance, security, fault tolerance, service quotas, and operational excellence. It is not a compliance-evidence automation service.

AWS Secrets Manager is incorrect because it stores, rotates, and manages secrets such as database credentials and API keys. It does not automate compliance assessments.

Amazon Inspector is incorrect because it scans workloads for software vulnerabilities and unintended network exposure. It is security-focused, but it does not create recurring evidence-mapped compliance assessments.

Therefore, the correct service is AWS Audit Manager.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): " Number of tokens consumed " : This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: " Temperature value " is incorrect as it affects the randomness of the model ' s output but not the cost directly.

Option C: " Amount of data used to train the LLM " is incorrect because training data size affects training costs, not inference costs.

Option D: " Total training time " is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.