IAPP AIGP - 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 Report 2025 elaborates:

“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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TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systemsimpact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or usedentirely outside of the EU—such as in theUK, unless they affect the EU market or individuals.

From theAI Governance in Practice Report 2025:

“The act imposes regulatory obligations… depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems.” (p. 7)

“The EU AI Act is the world’s first comprehensive AI regulation… requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market.” (p. 7–8)

Thus:

A. Open source releasedoes not exclude applicability if deployed in the EU.

B. Deployment at an EU borderclearly invokes jurisdiction.

D. Training by an EU companycreates jurisdictional links.

C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.

Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference: AIGP Body of Knowledge on AI Governance and Infrastructure.

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 Report 2025and 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.

The correct answer is D, model disgorgement. This technique refers to removing or eliminating the influence of improperly obtained, biased, or unlawfully used data from a trained machine learning model. It is increasingly discussed in AI governance and regulatory enforcement, particularly where models have been trained on data collected without proper consent or in violation of legal requirements. Instead of merely cleaning datasets, model disgorgement may require retraining or discarding the model entirely to ensure that problematic data no longer influences outputs. This aligns with accountability and compliance principles in AI governance, where organizations must ensure lawful data use throughout the AI lifecycle. Other options like data cleansing and de-duplication address data quality but do not fully remove learned patterns already embedded in trained models.

The correct answer is C because understanding algorithmic methodology is primarily a design, development, and model evaluation activity rather than part of a deactivation or sunset risk strategy. A deactivating risk strategy focuses on safely retiring or discontinuing an AI system, ensuring that operational, legal, and business risks are properly managed. This includes evaluating regulatory requirements such as data retention and deletion obligations, assessing business continuity and financial implications, and understanding dependencies with upstream and downstream systems to avoid disruption. These steps ensure a controlled and compliant system shutdown. In contrast, analyzing algorithmic methodology is relevant during system development, validation, and explainability efforts, not during the decommissioning phase of the AI lifecycle.

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.

A best reflects practical application of data minimization to machine learning. Data minimization requires personal data to be adequate, relevant, and limited to what is necessary for the specified purpose. AI development does not categorically prohibit using personal data in training, so B is too absolute. Personal data may legitimately be required during both training and inference, making D incorrect. Manual review of every data item is neither the definition nor a necessary implementation of minimization. Privacy-enhancing techniques can instead reduce the amount or identifiability of personal data processed while retaining model utility. ICO AI guidance specifically identifies perturbation or adding noise, synthetic data, and federated learning as techniques that can support data minimization during model training. Thus, A is the technically and governance-aligned choice.

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The correct answer is D because Privacy Impact Assessments are already structured processes designed to identify risks related to data use, processing, and potential harm to individuals. These assessments can be readily adapted to evaluate AI-specific risks, such as bias, automated decision-making impacts, and data protection concerns. AI governance frameworks emphasize leveraging existing compliance mechanisms to efficiently integrate AI risk management without duplicating processes. Privacy impact assessments align closely with AI risk evaluation because they examine how personal data is collected, used, and protected throughout the system lifecycle. Other options, such as penetration testing or training, focus on narrower objectives like security or awareness and are not comprehensive tools for identifying broader AI risks. Adapting PIAs supports a risk-based, scalable, and governance-aligned approach to managing AI systems.

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.