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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems |
| Topic 2: Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Data protection and privacy in AI workflows |
| Topic 3: Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation |
| Topic 4: Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Topic 5: Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications - Describe use cases for text, image, video, and code generation |
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NEW QUESTION # 347
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: B
NEW QUESTION # 348
An AI practitioner is using an LLM-as-a-judge in Amazon Bedrock to evaluate the quality of agent responses in a production environment. The AI practitioner wants to apply a built-in metric that assesses how thoroughly the agent responses address all parts of each prompt or question.
Which metric will meet these requirements?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
In Amazon Bedrock evaluations, Completeness measures how thoroughly a model or agent response addresses all aspects of the user prompt.
AWS evaluation guidance for LLM-as-a-judge explains that:
* Completeness focuses on coverage of prompt requirements
* It is especially useful for evaluating multi-part questions
* It is a built-in qualitative metric in agent evaluation workflows
Why the other options are incorrect:
* ROUGE (A) measures text overlap, mainly for summarization.
* Following instructions (C) evaluates adherence, not coverage.
* Refusal (D) measures appropriate refusal behavior.
AWS AI document references:
* Amazon Bedrock Model Evaluation
* LLM-as-a-Judge Metrics
* Evaluating Agent Responses on AWS
NEW QUESTION # 349
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
Answer: C
Explanation:
The correct answer is A because ensuring responsibility and fairness in ML begins with bias detection in the training data. Including a balanced representation of all demographics ensures the model learns fairly across different groups, which is critical in regulated industries like finance.
From AWS documentation:
"A key principle of responsible AI is building models that do not propagate or amplify bias. Fairness begins with training data. Reviewing and augmenting data for representation is essential." Explanation of other options:
B). The number of hidden layers doesn't inherently improve fairness or responsibility.
C). Keeping decisions opaque violates explainability principles in responsible AI.
D). A static dataset can become outdated and may not reflect real-world shifts, which limits fairness assessment over time.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon SageMaker Clarify Documentation - Bias Detection and Explainability
* AWS Responsible AI Guidelines
* AWS ML Specialty Study Guide - Fairness and Governance
NEW QUESTION # 350
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service meets this requirement?
Answer: B
Explanation:
The correct answer is AWS Artifact, which is a central resource for accessing AWS compliance documentation, audit reports, and certifications from both AWS and third-party providers, including ISVs.
According to AWS documentation, Artifact supports notifications for newly available reports. Customers can subscribe to notification emails when specific compliance documents or updates are published. This feature is ideal for regulated industries or companies needing third-party verification of compliance and security postures. Artifact provides access to reports such as SOC 2, ISO, PCI, and more, and is commonly used for audit preparation and vendor risk management. In contrast, AWS Audit Manager helps create internal audit frameworks but is not focused on third-party report distribution. AWS Data Exchange is for marketplace data sharing, and Trusted Advisor provides performance and cost optimization recommendations, not compliance document alerts. Thus, AWS Artifact is the purpose-built service that meets this requirement.
Referenced AWS AI/ML Documents and Study Guides:
AWS Artifact Documentation - Compliance Reports and Subscriptions
AWS Security and Compliance Whitepaper - Using Artifact for Third-Party Assurance
NEW QUESTION # 351
A financial company is creating an AI model for customer loan applications. The company wants to demonstrate the principles of human-centered design for explainable AI.
Which Amazon SageMaker AI feature meets these requirements?
Answer: D
Explanation:
The verified answer is B. Amazon SageMaker Clarify. The model is used for customer loan applications, where explainability, transparency, and human-centered decision support are critical. AWS documentation states that SageMaker Clarify provides tools to help explain how machine learning models make predictions. These tools help ML developers, modelers, and internal stakeholders understand model characteristics before deployment and debug predictions after deployment.
SageMaker Clarify is specifically aligned with explainable AI because it provides feature attribution methods, including SHAP-based explanations. AWS documentation explains that Clarify can generate reports that contribute to model governance documentation and can help describe how a model works, what features influence its decisions, and how explanations are generated.
Amazon SageMaker Model Registry is incorrect because it manages model versions, approval status, and deployment tracking. It supports governance workflows, but it does not explain why a model made a decision.
Amazon SageMaker Pipelines is incorrect because it automates and orchestrates ML workflows such as training, evaluation, and deployment. It does not provide the core explainability function required here.
Amazon SageMaker Feature Store is incorrect because it stores, shares, and serves ML features for training and inference. It improves feature reuse and consistency, but it does not explain model decisions.
NEW QUESTION # 352
......
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