IAPP Updated AIGP Exam Questions and Answers by zack

The correct answer is A because evaluating the effectiveness of an AI system requires measuring how well its outputs align with real-world outcomes. In this case, comparing predicted ad engagement with actual user clicks provides a direct measure of model performance and business impact. AI governance frameworks emphasize outcome-based monitoring to ensure systems meet their intended objectives and deliver value. Tracking output performance also supports continuous improvement by identifying gaps between predictions and reality. Options B and C focus on indirect indicators, such as internal model behavior or input targeting, which do not fully capture effectiveness. Option D relates to infrastructure performance rather than model outcomes. Monitoring output accuracy and real-world results is essential for validating AI system effectiveness and ensuring accountability.

Before offering an AI solution in the EU market, a Canadian company must take several steps to comply with the EU AI Act. These steps include establishing a risk and quality management system (B), engaging a third-party auditor to perform a bias audit (C), and drawing up technical documentation and instructions for use (D). However, there is no requirement to register the AI solution in a public EU database (A). This registration step is not specified as part of the compliance requirements under the EU AI Act for such solutions​​​​.

Bias in AI can be reduced during the planning and design phases through stakeholder involvement, human oversight, and careful data collection. While feature selection is critical in the development phase, it does not specifically occur during planning and design. Ensuring diverse stakeholder involvement and human oversight helps identify and mitigate potential biases early, and data collection ensures a representative dataset. Reference: AIGP Body of Knowledge on AI Development Lifecycle and Bias Mitigation.

The correct answer is B because ISO/IEC 42005 emphasizes integrating AI risk management into existing enterprise risk management frameworks rather than creating siloed or duplicative processes. Effective AI governance requires embedding AI-specific risks into established governance structures such as risk registers, controls, and monitoring systems. This ensures consistency, scalability, and alignment with organizational risk practices. Options C and D introduce parallel or fragmented processes, which contradict the principle of integrated governance and may create inefficiencies or gaps. Option A focuses only on data collection and does not address governance integration. The AI Governance in Practice Report highlights that organizations should incorporate AI risks into broader enterprise risk management and maintain unified risk registers to ensure visibility, accountability, and coordinated mitigation across the organization.