Free IAPP AIGP Practice Exam with Questions & Answers | Set: 4

In the context of AI, particularly generative models, " hallucination " refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.

When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.

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.

Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference: AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails)​​.

External communication plans primarily establish how an organization communicates relevant AI information, risks, incidents, limitations, deployment changes, and governance matters to people outside the organization. A therefore provides the broadest and most accurate purpose. The current IAPP AIGP Study Guide expressly treats external communications as part of ongoing AI operations and associates them with transparency and trust-building. NIST AI RMF likewise requires communication plans for identified and measured AI risks and encourages communication with external stakeholders, affected communities, users, and other relevant AI actors. B concerns internal training rather than external communication. C belongs to third-party procurement and contract governance. Regulatory notification in D may form one component of an external communication plan when legally required, but it is considerably narrower than the overall governance objective of transparent stakeholder communication represented by A.

The correct answer is Explainable Ai because it specifically refers to the ability of a system to describe the logic behind its decisions or output in a way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressing the black-box problem in AI.

From the AIGP ILT Participant Guide (Module on Transparency and Explainability):

“Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems.”

Also, according to the AI Governance in Practice Report 2025:

“Explainability refers to the representation of the underlying mechanisms of the AI system’s operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained.”

Thus, while Trustworthy and Responsible AI are broader concepts,explainabilityspecifically targets the regulatory concern about understanding outputs.

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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 Report 2025:

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

The correct answer is D because the company has already addressed several key responsible AI principles, including fairness through discrimination risk mitigation, robustness through adversarial and edge-case testing, and reliability via repeatability testing. However, there is no indication that the company has addressed transparency, which involves providing clear information about how the model operates, how pricing decisions are made, and how outputs may affect consumers. Transparency is a critical AI governance principle, especially in consumer-facing applications like dynamic pricing, where decisions can significantly impact individuals. Governance frameworks emphasize that users and stakeholders should be informed about AI-driven decisions and their implications. Without transparency, even technically sound and fair systems may lack trustworthiness and fail to meet regulatory or ethical expectations.

The correct answer is A because having a well-defined incident management process enables organizations to respond quickly and effectively when AI-related issues arise. AI governance frameworks emphasize incident management plans as a key component of operational governance, ensuring that risks such as system failures, harmful outputs, or security breaches are promptly identified, escalated, and resolved. A structured process reduces delays by clearly defining roles, responsibilities, and response procedures, thereby minimizing potential harm and operational disruption. While incident processes may also indirectly support compliance and reduce the impact of failures, their primary benefit is improving responsiveness and coordination. Efficient response times are critical in maintaining trust, ensuring safety, and limiting negative consequences in real-world AI deployments.

The GDPR ' s transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.