IAPP Updated AIGP Exam Questions and Answers by bianka

The primary reason the EU is considering updates to its Product Liability Directive is to address digital services and connected products. The current directive does not adequately cover the complexities and challenges posed by modern digital and connected technologies. By updating the directive, the EU aims to ensure that it remains relevant and effective in addressing the liabilities associated with these advanced products, ensuring consumer protection and fair market practices in the digital age.

Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model. Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.

An AI system’sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.

From theAI Governance in Practice Report 2025:

“There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization’s role, footprint, broader risk-governance profile and maturity.” (p. 4)

“AI governance starts with defining the corporate strategy for AI… and formulating policy standards and operational procedures to reflect industry, use case, and location.” (p. 11)

A– Organizational accountability is broader and not directly scoped by industry or function.

C– Diversity of data sources is tied to data strategy.

D– Explainability is more influenced by model type, not use context.

A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI. Reference: AIGP Body of Knowledge on Impact Assessments.