AIGP Exam Dumps - IAPP Artificial Intelligence Governance Questions and Answers

Deployers of proprietary models arenot responsible for design, but they are accountable for how the system performsin their context of use, including ensuring ethical behavior, performance, and legal compliance.

From theAI Governance in Practice Report2025:

“Deployers of AI systems must take reasonable steps to ensure that systems are used ethically, perform safely, and align with applicable laws and standards.” (p. 11–12)

“Operational governance… includes performance monitoring protocols, incident management plans, and regulatory oversight.” (p. 12)

Thus:

✅A. Ethical testing– Required to mitigate misuse and unintended harms.

❌B. Ethical design– Belongs todevelopers/providers, not deployers.

✅C. Technical performance– Deployers must ensure that AI performs as expected.

❌D. System documentation– This is theprovider’sobligation.

✅E. Regulatory compliance– Deployers must ensure system use complies with applicable laws.

Conformity assessmentsare a core requirement under theEU AI Actfor high-risk systems and serve to confirm that the AI meetsregulatory, safety, and ethical standardsbefore it is put into production.

From theAI Governance in Practice Report2025:

“Conformity assessments… ensure that systems comply with legal requirements, safety criteria, and intended purpose before being placed on the market.” (p. 34)

“They are a critical step to demonstrate safety and trustworthiness in AI deployment.” (p. 35)

The most critical reason for documenting AI-related risks is toreduce exposure to legal, regulatory, and reputational liabilities. Clear documentation demonstrates thatrisks were identified, assessed, and addressed, which is essential for accountability and defensibility in the face of audits, litigation, or enforcement actions.

From theAI Governance in Practice Report2025:

“An effective AI governance model is about collective responsibility… which should encompass oversight mechanisms such as privacy, accountability, compliance.” (p. 13)

“Accountability… is based on the idea that there should be a person or entity that is ultimately responsible for any harm resulting from the use of the data, algorithm and AI system's underlying processes.” (p. 28)

While transparency, alignment with standards, and knowledge sharing are all secondary benefits,risk documentation’s primary role is liability mitigation.

The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.

Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity. Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.

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.

When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.

From theAI Governance in Practice Report2025:

“Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems.” (p. 12, 35)

When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles. Reference: AIGP Body of Knowledge on AI Governance and Compliance Integration.