Passed Exam Today AIF-C01

The verified answer is A. Amazon Bedrock Guardrails. AWS documentation states that Amazon Bedrock Guardrails helps detect sensitive information, including personally identifiable information, in input prompts or model responses by using sensitive information filters. AWS also states that these filters can be configured to block or mask detected PII. This directly matches the requirement to prevent a customer service agent from disclosing PII during conversations.

Bedrock Guardrails supports handling sensitive information in two key ways. A block action can block requests or responses that include sensitive information, and a mask action can anonymize or redact detected information in model requests or responses. AWS gives examples of PII categories such as names, email addresses, phone numbers, usernames, passwords, addresses, credit card numbers, PINs, and other sensitive identifiers. For a customer service agent, this is exactly the right feature because PII could appear in either the user input or the model response.

Amazon Bedrock Flows is incorrect because flows help orchestrate generative AI workflows. They do not primarily provide PII filtering. Amazon Bedrock Knowledge Bases is incorrect because knowledge bases support Retrieval Augmented Generation by connecting models to enterprise data sources. They help ground responses but do not directly block PII disclosure. Amazon Bedrock Data Automation is incorrect because BDA is used to transform and extract insights from multimodal content such as documents, images, audio, and video. It is not the runtime guardrail feature for blocking or masking PII in conversations.

Therefore, the correct Amazon Bedrock feature is Amazon Bedrock Guardrails.

The verified answer is C. Add unified governance, discovery, and collaboration capabilities to Amazon SageMaker AI. The question is not asking only for storage, monitoring, or a basic repository. It asks for a scalable way for employees to find, access, and properly use company data while remaining compliant with policies and standards. AWS documentation for the next generation of Amazon SageMaker states that it simplifies discovery, governance, and collaboration for data and AI. Amazon SageMaker Catalog helps users securely discover and access approved data and assets through semantic search and governance workflows.

This directly matches the company’s requirement. In a rapidly growing multinational organization, data becomes harder to locate, understand, govern, and reuse. Unified governance and catalog capabilities allow teams to publish, discover, request access to, and collaborate on approved assets. AWS also describes Amazon SageMaker Catalog as a way to discover, govern, and collaborate on structured data, unstructured data, AI models, BI dashboards, and applications from a single catalog.

Option A is incorrect because a repository without integration does not solve governance, collaboration, or policy-based access. Option B is incomplete because storage alone does not help employees find or properly use data. Option D is also incomplete because monitoring can detect issues, but it does not provide data discovery, governance workflows, or collaboration features.

The correct solution must combine discovery, access governance, metadata, collaboration, and policy-aligned usage. Therefore, option C is the correct answer.

Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task—in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model’s outputs.

C is correct:

“Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case.”

(Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide)

A is incorrect—unsupervised learning does not use labeled data.

B (continued pre-training) uses unlabeled data.

D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.

Adjusting the prompt is the correct solution to align the LLM outputs with the company ' s expectations for short, specific language responses.

Adjust the Prompt:

Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.

A well-crafted prompt can provide specific instructions to the model, such as " Answer in a short sentence in Spanish. "

Why Option A is Correct:

Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.

Flexibility: Prompt engineering is a flexible approach to refining the model’s behavior without modifying the model itself.

Why Other Options are Incorrect:

B. Choose an LLM of a different size: The model size does not directly impact the response length or language.

C. Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.

D. Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.