AIGP Exam Dumps - Artificial Intelligence Governance Professional

The correct answer isB – Procedural. TheOECD Framework for Classifying AI Systemscategorizescodes of conduct and collective agreementsasprocedural toolsbecause they guide internal governance and decision-making processes.

From the AIGP ILT Participant Guide – Global Governance Models:

“Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement.”

AI Governance in Practice Report2025elaborates:

“These procedural tools support internal accountability mechanisms and ethics compliance frameworks… they are part of soft governance.”

These toolsdo not measure or analyze technical performance, hence they arenot technical or analytic.

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Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.

According to definitions and discussions from theAI Governance in Practice Report2025and U.S. privacy frameworks:

“Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition.” (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.)

Here’s how the options relate:

A. Iris scans– These are physical biometric identifiers.

B. Walking gait– Behavioral biometric used increasingly in surveillance and identification.

C. Keystroke dynamics– Behavioral biometric based on typing patterns.

D. GPS location of a user's fitness watch– This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company’s competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.

Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline—allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.

Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high-stakes environmentswhere hallucinations are a concern.

B, C, and Ddescribe optimization or bias mitigation—not the core function of RAG.

Model disgorgement is the technique used to remove the effects of improperly used data from an ML system. This process involves retraining or adjusting the model to eliminate any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's outputs are not influenced by data that was not meant to be used or was used incorrectly. Reference: AIGP Body of Knowledge on Data Management and Model Integrity.

The best reason for the police department to continue performing investigations even if the AI system scores an individual's likelihood of criminal activity at or above 90% is that AI systems affecting fundamental civil rights should not be fully automated. Human oversight is essential to ensure that decisions impacting civil liberties are made with due consideration of context and mitigating factors that an AI might not fully appreciate. This approach ensures fairness, accountability, and adherence to legal standards. Reference: AIGP Body of Knowledge on AI Ethics and Human Oversight.

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.