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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle |
| Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
| Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety |
| Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Data protection and privacy in AI workflows - Governance frameworks for AI lifecycle - Security controls for AI data and models |
| Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
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NEW QUESTION # 396
Which AWS feature records details about ML instance data for governance and reporting?
Answer: B
Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
NEW QUESTION # 397
A company is deploying a generative AI (GenAI) assistant to explain policies to customers. The company wants to use Amazon Bedrock Guardrails to validate that the GenAI assistant's responses comply with company policies.
Which Guardrails feature will meet this requirement?
Answer: B
Explanation:
Automated Reasoning checks are specifically designed to determine whether a foundation model's response is logically consistent with policies, rules, and requirements supplied by an organization. AWS states that Automated Reasoning checks "use mathematical techniques to validate natural language content against policies you define." This directly matches the scenario. The organization has company policies, and the AI assistant generates explanations of those policies. The requirement is not simply to remove harmful language or verify that the response came from retrieved source material. Instead, the company needs to validate whether the generated answer conforms to its defined policy rules.
Amazon Bedrock Automated Reasoning converts relevant natural-language content into formal logical representations and evaluates the model's claims against an Automated Reasoning policy. AWS explains that the resulting findings can identify statements that contradict policy rules, expose unstated assumptions, and provide verifiable reasoning about whether a response is consistent with the policy. The result can be classified as valid, invalid, satisfiable, or another logical outcome.
Content filters primarily detect categories of undesirable or harmful content, including configurable prompt-attack detection. They do not formally prove compliance with a company's business rules.
Contextual grounding checks determine whether responses are grounded in supplied source material and relevant to a user's query. This is particularly useful for detecting hallucination in RAG applications, but it is not the same as validating logical compliance with formal company policies.
Sensitive information filters detect information such as personally identifiable information and configured sensitive data patterns.
Therefore, because the organization specifically wants to validate AI-generated policy explanations against its defined policies, Automated Reasoning checks provide the purpose-built Amazon Bedrock Guardrails capability.
NEW QUESTION # 398
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?
Answer: A
NEW QUESTION # 399
A company deployed AI agents to automate manual decision-making processes.
Which metric measures the immediate value of this deployment?
Answer: A
Explanation:
The verified answer is B. Reduction of the cost for each decision. The question asks for the immediate value of deploying AI agents to automate manual decision-making. AWS Prescriptive Guidance for operationalizing agentic AI identifies immediate ROI metrics such as cost per decision, time compression, and error reduction. Cost per decision compares the cost of agent processing against the human equivalent. That directly maps to the scenario because the company replaced or augmented manual decision-making processes with AI agents.
The immediate value of automation is usually measured by direct operational efficiency: lower cost, faster cycle time, fewer manual steps, and reduced human effort. AWS guidance also recommends anchoring technical and business KPIs to strategic value, including decision latency, resolution accuracy, and cost savings. This supports using cost per decision as a practical business metric for agent deployments.
Option A is incorrect because network effect is a long-term platform or ecosystem metric. It measures the increasing value of a product as more users or participants join, not the immediate value of automating decisions. Option C is incorrect because additional revenue from market expansion is a strategic growth outcome, not an immediate operational value metric. Option D is also incorrect because additional vertical solutions represent future expansion potential, not the direct value of the current deployment.
NEW QUESTION # 400
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
Answer:
Explanation:
Explanation:
This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.
Scenario 1:
Encrypt the application data, and isolate the application on a private network.
# Principle: Privacy and Security
From AWS documentation:
"Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance." Scenario 2:
Evaluate how different population groups will be impacted.
# Principle: Fairness
From AWS documentation:
"The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected." Scenario 3:
Test the application with unexpected data to ensure the application will work in unique situations.
# Principle: Robustness
From AWS documentation:
"Robustness refers to an AI system's ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases." Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Practices Whitepaper - Core Principles of Responsible AI Amazon Bedrock Documentation - Responsible AI and Safety Controls AWS Certified Machine Learning Specialty Guide - AI Governance and Model Evaluation
NEW QUESTION # 401
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