AIGP Exam Dumps - IAPP Artificial Intelligence Governance Questions and Answers

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 deceptive 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.

The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual ' s intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.

The correct answer isD – Privacy impact assessments (PIAs). These aredirectly adaptablefor identifying risks in AI systems, particularly around data usage, bias, and individual impacts.

From the AIGP ILT Guide – Risk Management Module:

“PIAs and DPIAs are existing tools used in privacy compliance that can be extended to evaluate the risks of AI, including fairness, explainability, and legality.”

AI Governance in Practice Report 2025 further explains:

“Organizations can adapt privacy impact assessments to evaluate the ethical, legal, and technical risks posed by AI systems. They provide a structured and recognized method.”

PIAs are preferable over general security practices (like pen testing) which do not address algorithmic bias or legal compliance directly.

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Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute — especially when such data is used to train foundational AI models.

From theAI Governance in Practice Report 2025:

“As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management.” (p. 19)

“Legal due diligence may include verification of the personal data ' s lawful collection by the databroker...” (p. 19)

This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.

A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.

B. The tech companymay train the model but depends on lawful sourcing by data aggregators.

The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation. A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €15 million or 3% of annual global turnover.

From the AIGP ILT Guide – EU AI Act Module:

“Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover.”

AI Governance in Practice Report 2025 supports this:

“Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article 71(3) of the EU AI Act.”

Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.

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According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act ' s framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards. High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act ' s provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting " strong " general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.

Under the EU AI Act, organizations that develop and deploy high-risk AI systems are required to provide several key disclosures to ensure transparency and accountability. These include the human oversight measures employed, how individuals can contest decisions made by the AI system, and informing individuals that an AI system is being used. However, there is no specific requirement to disclose the exact locations where data is stored. The focus of the Act is on the transparency of the AI system ' s operation and its impact on individuals, rather than on the technical details of data storage locations.

Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in,classic ML models(e.g., regression, decision trees, random forests) are optimal.

From theAI Governance in Practice Report 2025:

“Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance.” (p. 33)

AandCmay have limited transparency and are often tied to specific providers.

Dinvolves ongoing costs and limited model control.

The correct answer isA. While TEVV is important inlater lifecycle phases, it isnot the primary considerationwhen evaluating and prioritizinguse cases.

From the AIGP Body of Knowledge – Use Case Assessment Module:

“Use case evaluation focuses on value, impact, fairness, and accessibility—technical testing considerations come later.”

ILT Guide confirms:

“Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization.”

Thus,Ais not a top-level consideration during use caseselection.

The correct answer isB. " Candidate quality " isnota negative consequence of using AI—rather, it is the intendedbenefitof using such tools (e.g., more efficient filtering of strong candidates).

From the AIGP ILT Guide:

“Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring. These risks may arise when AI models replicate biases present in training data or obscure the decision logic.”

AI Governance in Practice Report 2025 (Bias and Fairness Section) also warns:

“Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups.”

Candidate quality is agoal, not a risk, makingB the correct answerfor what isnota negative outcome.

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