Pass the IAPP Artificial Intelligence Governance AIGP Questions and answers with Dumpstech

ISO/IEC 42001:2023 establishes an Artificial Intelligence Management System designed to integrate AI governance into an organization ' s existing management structure rather than create an isolated governance regime. Its AI-policy requirements state that the policy should refer, where relevant, to other organizational policies. Therefore, A most accurately describes the relationship. The standard does not require the organization to absorb all related security, privacy, quality, risk, or compliance policies into one AI policy, making B incorrect. C is inconsistent with integrated governance because AI issues commonly intersect with information security, privacy, procurement, data governance, human resources, and enterprise risk management. Management review is an important management-system activity, but D does not state the relationship required between the AI policy and existing policies. ISO/IEC 42001 consequently favors coordinated and mutually aligned organizational governance.

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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.

C is the correct statement, subject to the statutory thresholds. Article 36 of South Korea ' s AI Basic Act requires an AI business operator without an address or business office in Korea to designate a domestic representative when it meets prescribed criteria concerning users, revenue, or other specified conditions. The implementing decree establishes those thresholds, including qualifying revenue and Korean-user levels. The Act also applies extraterritorially where conduct outside Korea affects the Korean market or users. Generative AI is not categorically excluded, eliminating B. The Korean framework does not simply reproduce the EU AI Act ' s prohibited-practices regime described in D, and " frontier models " are not established as the distinct regulatory category suggested by A. Therefore, domestic-representative requirements make C the best answer.

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Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.

Reviewing the terms of use is essential when gathering data from a clinical research partner. This step ensures that the healthcare network complies with all legal and contractual obligations related to data usage. It addresses data ownership, usage limitations, consent requirements, and privacy obligations, which are critical to maintaining ethical standards and avoiding legal repercussions. This review helps ensure that the data is used in a manner consistent with the agreements made and the regulatory environment, which is fundamental for lawful and ethical AI development. Reference: AIGP Body of Knowledge on Legal and Regulatory Considerations.

The correct answer is D. Key stroke dynamics, used to identify individuals, fall under biometric data, which is a special category of personal data under the GDPR and other frameworks.

From the AI Governance in Practice Report 2025:

“Keystroke dynamics may constitute biometric data if used to uniquely identify an individual… Biometric data is classified as special category personal data and requires higher protection standards.”

Also reflected in ILT Participant Guide:

“Biometric data, such as facial images, voiceprints, iris scans or keystroke patterns, are treated as special category data when they are used for the purpose of uniquely identifying individuals.”

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)​​.

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.

A, B, and E are the intended privacy-preserving choices. Data minimization limits processing to information that is adequate, relevant, and necessary for the defined purpose, reducing privacy exposure during AI development. Anonymization protects individuals by transforming information so that people are no longer identifiable under the applicable standard. Federated learning is a recognized privacy-enhancing technology that enables models to be trained across distributed datasets without centrally sharing the underlying raw training data. The ICO specifically identifies federated learning and related privacy-preserving methods as useful during AI training and explains the relationship between anonymization and data minimization. Data mapping is primarily a governance and inventory activity used to understand data flows, while data cleansing principally improves data quality by correcting or removing inaccurate, incomplete, or unsuitable records; neither is inherently a PET.

Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.