Amazon Web Services Updated AIF-C01 Exam Questions and Answers by laurie

SageMaker Clarify provides model explainability, bias detection, and helps visualize how input features affect predictions.

Canvas is for low-code ML building.

Feature Store is for storing and managing ML features.

Ground Truth is for data labeling.

???? Reference:

AWS Documentation – Amazon SageMaker Clarify

The correct answer is A because Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically detect sensitive data such as PII (personally identifiable information) in Amazon S3. It requires no custom development, and it can be configured to generate alerts when sensitive data is detected in newly uploaded objects.

From AWS documentation:

"Amazon Macie automatically discovers and classifies sensitive data in S3 buckets and generates alerts when it detects sensitive content, such as names, addresses, and credit card numbers."

Explanation of other options:

B. Deploying an LLM on SageMaker to perform redaction is custom and operationally intensive.

C. Regex-based detection is brittle and requires extensive manual work, with high maintenance overhead.

D. Asking customers to avoid sharing sensitive data is not enforceable and does not meet compliance or security standards.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Macie Documentation – Sensitive Data Discovery and Alerting

AWS Security Best Practices – Data Privacy and Governance

AWS ML Specialty Guide – Governance and Compliance Automation

Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.

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