IAPP Updated AIGP Exam Questions and Answers by zayne

The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation. A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €15 million or 3% of annual global turnover.

From the AIGP ILT Guide – EU AI Act Module:

“Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover.”

AI Governance in Practice Report 2025 supports this:

“Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article 71(3) of the EU AI Act.”

Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.

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The correct answer is D, model disgorgement. This technique refers to removing or eliminating the influence of improperly obtained, biased, or unlawfully used data from a trained machine learning model. It is increasingly discussed in AI governance and regulatory enforcement, particularly where models have been trained on data collected without proper consent or in violation of legal requirements. Instead of merely cleaning datasets, model disgorgement may require retraining or discarding the model entirely to ensure that problematic data no longer influences outputs. This aligns with accountability and compliance principles in AI governance, where organizations must ensure lawful data use throughout the AI lifecycle. Other options like data cleansing and de-duplication address data quality but do not fully remove learned patterns already embedded in trained models.

The correct answer is D because conformity requirements are primarily intended to ensure that AI systems meet applicable legal, regulatory, and safety standards before deployment. AI governance frameworks, including the EU AI Act and international standards, require conformity assessments to verify that systems are safe, reliable, and compliant with risk management, documentation, and performance obligations. These assessments help identify and mitigate risks prior to market release, particularly for high-risk AI systems that may impact individuals’ rights, health, or safety. Conformity ensures accountability, transparency, and trustworthiness, which are central principles of responsible AI governance. The other options relate to usability or technical considerations, but they do not address the pri mary purpose of conformity assessments, which is regulatory compliance and risk mitigation prior to deployment.

Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs— making it preferable when deep control is needed.

From theAI Governance in Practice Report 2025:

“Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies.” (p. 39)