The correct answer is D - F1 score, which is the harmonic mean of precision and recall. AWS documentation states that the F1 score is the preferred metric when evaluating ML models trained on imbalanced datasets, where one class appears significantly more often than others. Accuracy becomes misleading in these situations because a model can achieve high accuracy simply by predicting the majority class. Precision alone measures correctness of positive predictions, while recall measures completeness-neither fully captures class balancing performance. The F1 score provides a combined measurement that reflects how well the model identifies minority classes while avoiding excessive false positives. AWS's ML Specialty Guide recommends using F1 for classification tasks where "class distribution is uneven or where both false negatives and false positives matter." This metric provides a fairer assessment of overall model performance across all class categories.
Referenced AWS Documentation:
AWS Certified Machine Learning Specialty Guide - Metrics for Imbalanced Classes Amazon SageMaker Developer Guide - Evaluating Classification Models

Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct:Content filters Avoid subjects related to illegal investment advice or legal advice:Denied topics Detect and block specific offensive terms:Word filters Detect and filter out information in the model's responses that is not grounded in the provided source information:Contextual grounding check The company is using a generative AI model on Amazon Bedrock and needs to mitigate undesirable and potentially harmful content in the model's responses. Amazon Bedrock provides several guardrail mechanisms, including content filters, denied topics, word filters, and contextual grounding checks, to ensure safe and accurate outputs. Each mitigation action in the hotspot aligns with a specific Bedrock filter policy, and each policy must be used exactly once.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
*"Amazon Bedrock guardrails provide mechanisms to control model outputs, including:
Content filters: Block harmful content such as hate speech, violence, or misconduct.
Denied topics: Prevent the model from generating responses on specific subjects, such as illegal activities or advice.
Word filters: Detect and block specific offensive or inappropriate terms.
Contextual grounding check: Ensure responses are grounded in the provided source information, filtering out ungrounded or hallucinated content."*(Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct: Content filtersContent filters in Amazon Bedrock are designed to detect and block harmful content, such as hate speech, insults, violence, or misconduct, ensuring the model's outputs are safe and appropriate. This matches the first mitigation action.
Avoid subjects related to illegal investment advice or legal advice: Denied topicsDenied topics allow users to specify subjects the model should avoid, such as illegal investment advice or legal advice, which could have regulatory implications. This policy aligns with the second mitigation action.
Detect and block specific offensive terms: Word filtersWord filters enable the detection and blocking of specific offensive or inappropriate terms defined by the user, making them ideal for this mitigation action focused on specific terms.
Detect and filter out information in the model's responses that is not grounded in the provided source information: Contextual grounding checkThe contextual grounding check ensures that the model's responses are based on the provided source information, filtering out ungrounded or hallucinated content. This matches the fourth mitigation action.
Hotspot Selection Analysis:
The hotspot lists four mitigation actions, each with the same dropdown options: "Select...," "Content filters,"
"Contextual grounding check," "Denied topics," and "Word filters." The correct selections are:
First action: Content filters
Second action: Denied topics
Third action: Word filters
Fourth action: Contextual grounding check
Each filter policy is used exactly once, as required, and aligns with Amazon Bedrock's guardrail capabilities.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Configuring Guardrails (https://aws.amazon.com/bedrock/)

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.

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 correct answer is Amazon Bedrock, AWS's fully managed service for building and scaling generative AI applications using foundation models (FMs). Bedrock gives developers access to models from leading providers such as Anthropic (Claude), Meta (Llama), Mistral, Cohere, and Amazon Titan. Users can invoke these models via API without managing infrastructure or model training. According to AWS documentation, Bedrock supports tasks such as text generation, summarization, question answering, image generation, and RAG workflows with minimal setup. It supports both on-demand and provisioned throughput modes and integrates with features like Guardrails, Knowledge Bases, and Agents for secure, enterprise-grade applications. Amazon Q Developer is a generative AI tool for developers, but it doesn't host or scale models.
Amazon Kendra is an intelligent search engine, and Amazon Comprehend is used for NLP tasks like entity extraction-not foundation model access.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Foundation Models and Use Cases
AWS Certified ML Specialty Guide - Generative AI on AWS