IAPP AIGP - Artificial Intelligence Governance Professional

Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model ' s capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.

Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking forbias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.

From theAI Governance in Practice Report 2025:

“Performance monitoring protocols… should include mechanisms to assess and measure disparities in outcomes across different demographic groups.” (p. 12)

“Bias may not be evident during pre-deployment testing but can emerge in real-world use.” (p. 41)

B. Awareness trainingis helpful, but not a technical bias mitigation activity.

C. Analyzing training datais apre-deploymenttask.

D. Documenting human decisionsmay support auditability but doesn’t detect bias in AI outputs.

Risk impact estimation occurs in the design phase of the AI life cycle. This step involves evaluatingpotential risks associated with the AI system and estimating their impacts to ensure that appropriate mitigation strategies are in place. It helps in identifying and addressing potential issues early in the design process, ensuring the development of a robust and reliable AI system. Reference: AIGP Body of Knowledge on AI Design and Risk Management.

Offeringloan products based on current offerings and rulesrequires a system that can followexplicit business logic, not generate open-ended content. Anexpert system, which is a rules-based AI that uses “if-then” logic, is ideal here.

From the AI governance context:

“Rule-based AI systems are often preferred when decisions must adhere to precise regulatory or financial criteria.” (aligned with AI best practices in regulated sectors)

A. RAGis used to integrate external knowledge—not suitable for structured, rule-based logic.

B. Multimodal modelshandle varied input types—not needed here.

D. Quantum computingis not yet practical or relevant for this business use case.

The correct answer isD. Transparency obligations under data protection laws, such as GDPR and most AI governance frameworks, require thatusers whose data is being processedbe directly informed.

From the AIGP ILT Guide (Privacy Module):

“The user privacy notice must be updated to explain the nature of automated processing, the logic involved, and the significance and consequences for the data subject.”

Also, per AI Governance in Practice Report 2025 (Part III):

“Transparency obligations apply throughout the lifecycle of AI… Individuals must be informed about automated decision-making and profiling that may impact them.”

Unlike internal policies or general privacy notices,the user privacy noticeprovides direct transparency to theindividual data subjectsaffected by AI processing.

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The U.S. mortgage company ' s AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).

Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model ' s predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.

AI models are intrinsically dependent on the data used to train, validate, and test them. NIST specifically identifies AI ' s inherent dependence on training data and links model reliability to data quality and representativeness. Therefore, A is correct. Training-data characteristics—including modality, structure, features, quantity, and complexity—also influence the appropriate model design and architecture, supporting D. E is correct because biases, gaps, nonrepresentative samples, and other limitations in training data can become embedded in model behavior and subsequently affect outputs. Privacy-enhancing technologies may reduce privacy risks, but they do not define data dependency, eliminating B. Likewise, improving dataset size and quality can improve performance but does not remove the model ' s fundamental dependence on data, so C is incorrect.

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The correct answer is C, the Federal Trade Commission. The FTC plays a central role in U.S. AI governance by enforcing consumer protection and unfair or deceptive practices laws. In the context of AI systems such as companion chatbots, the FTC has authority to investigate risks related to user harm, transparency, safety, and misleading claims. AI governance frameworks emphasize regulatory oversight where AI systems may impact individuals psychologically, financially, or socially. Agencies like the FTC focus on ensuring that companies deploying AI systems do not cause harm through unsafe or de ceptive practices and that they provide adequate disclosures about risks and system behavior. This aligns with broader AI governance principles of accountability, transparency, and risk management highlighted in the AI Governance in Practice Report 2024 , which stresses the importance of regulatory involvement in managing AI-related risks.

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 Report 2025:

“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.