AIGP Exam Practice Questions - Volume 2 (Chapters 7-13)

AIGP Certification Practice: Mastering AI Governance (Chapters 7-13)

Prepare thoroughly for the AI Governance Professional (AIGP) certification with this comprehensive practice exam resource covering chapters 7 through 13. This course provides realistic practice questions designed to mirror the style, difficulty, and scope of the official AIGP exam. Each question is accompanied by detailed explanations to build your understanding and confidence in AI governance.

Important Note: This course is solely a practice exam resource and is not affiliated with or endorsed by the certification body. Successful completion of these questions does not guarantee passing the AIGP exam. For Part 1 (Chapters 1-6), please refer to the "AIGP Exam Practice Questions – Volume 1" course.

This volume focuses on crucial aspects of AI governance, including risk management, compliance, transparency, and accountability throughout the AI model lifecycle. Each chapter contains 50 questions (Chapters 12 & 13 combined into one practice test), offering a robust assessment of your knowledge.

Here's a breakdown of the topics covered:

Chapter 7: Industry Standards & Frameworks

• Understand key industry standards and tools relevant to artificial intelligence (AI).
• Explore the OECD principles, framework, policies, and recommended practices for trustworthy AI.
• Master the NIST AI Risk Management Framework and Playbook, including core functions, categories, and subcategories.
• Learn about the NIST ARIA program for AI safety methodologies, tools, metrics, and measurements.
• Understand core ISO AI standards (ISO/IEC 22989 and ISO/IEC 42001).

Chapter 8: AI Model Design & Development Governance

• Define the business context and use case for AI models.
• Conduct or review impact assessments on AI models.
• Identify applicable laws and regulations governing AI models.
• Apply policies, procedures, best practices, and ethical considerations to AI model design and development (e.g., purpose, requirements, architecture, human oversight, data analysis, metrics, stakeholder engagement, operational controls).
• Identify and manage internal and external risks related to AI model development (e.g., using probability/severity matrices, risk mitigation hierarchies, stakeholder mapping, benchmarking, pre-deployment testing).
• Document the AI model design and development process for compliance and risk management.

Chapter 9: Data Governance for AI Model Training & Testing

• Establish and adhere to data governance requirements (lawful rights to collect & use data, data quality, quantity, integrity, and fit-for-purpose).
• Establish and document data lineage and provenance.
• Plan and execute AI model training and testing (unit, integration, validation, performance, security, bias, and interpretability).
• Identify and manage issues and risks during AI model training and testing.
• Document the AI model training and testing process for validation, compliance, and risk management.

Chapter 10: AI Model Release, Monitoring & Maintenance

• Assess readiness and prepare for AI model release into production (e.g., creating model cards, satisfying conformity requirements).
• Conduct continuous monitoring and establish a maintenance schedule for AI models.
• Perform periodic assessments of AI model performance, reliability, and safety (e.g., audits, red teaming, threat modeling, security testing).
• Manage and document incidents, issues, and risks related to AI models.
• Collaborate with stakeholders to understand root causes of AI model incidents (e.g., brittleness, data drift).
• Make public disclosures to meet transparency obligations (e.g., technical documentation, deployment instructions, post-market monitoring plans).

Chapter 11: Evaluating AI Model Deployment Factors & Risks

• Understand the context of the AI use case (business objectives, performance requirements, data availability, ethical considerations, workforce readiness).
• Differentiate between AI model types (classic vs. generative, proprietary vs. open source, small vs. large, language vs. multimodal).
• Understand AI deployment options (cloud vs. on-premise vs. edge, fine-tuning, retrieval augmented generation).

Chapter 12: AI Model Assessment & Risk Evaluation

• Perform or review impact assessments on selected AI models.
• Identify and evaluate applicable laws governing AI models.
• Analyze key terms and risks in vendor or open