IAPP Updated AIGP Exam Questions and Answers by bianka

The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

The correct answer is A. The ARIA program by NIST is explicitly designed to support stakeholders in understanding and managing the risks and impacts of AI systems.

From the AIGP ILT Guide – U.S. Risk Frameworks Module:

“NIST’s ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use.”

Also cited in the AI Governance in Practice Report 2024 (Frameworks Section):

“ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications.”

ARIA is not a red-teaming or sandbox program—it’s an assessment and governance resource.

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The correct answer is A. While TEVV is important in later lifecycle phases, it is not the primary consideration when evaluating and prioritizing use cases.

From the AIGP Body of Knowledge – Use Case Assessment Module:

“Use case evaluation focuses on value, impact, fairness, and accessibility—technical testing considerations come later.”

ILT Guide confirms:

“Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization.”

Thus, A is not a top-level consideration during use case selection.

An AI model is best defined as a program that has been trained on a set of data to find patterns within that data. This definition captures the essence of machine learning, where the model learns from the data to make predictions or decisions. Reference: AIGP BODY OF KNOWLEDGE, which provides a detailed explanation of AI models and their training processes​​.