IAPP Updated AIGP Exam Questions and Answers by mary

Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects). Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry. Reference: AIGP BODY OF KNOWLEDGE and AI development frameworks.

The November 2023 White House Executive Order provides guidance that governmental agencies should limit access to specific uses of generative AI. This means that generative AI tools should be used in a controlled manner, where their applications are restricted to well-defined, approved use cases that ensure the security, privacy, and ethical considerations are adequately addressed. This approach allows for the benefits of generative AI to be harnessed while mitigating potential risks and abuses.

The planning phase of the AI life cycle typically includes defining the objective of the model, choosing the appropriate architecture, and understanding the context in which the model will operate. However, the approach to governance is usually established as part of the overall AI governance framework, not specifically within the planning phase. Governance encompasses broader organizational policies and procedures that ensure AI development and deployment align with legal, ethical, and operational standards. Reference: AIGP Body of Knowledge, AI lifecycle planning phase section.

The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.

From theAI Governance in Practice Report 2025:

“It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations.” (p. 34)

“To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle.”

“Conducting post-market monitoring” and “conformity assessments” areexplicitrequirements for high-risk systems. (p. 34–35)

However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.

A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.