IAPP AIGP Exam Dumps

The correct answer isD. Personalized content and advertisements, as long as properly disclosed and non-deceptive, arenot generally a consumer protection issueunder current legal regimes.

From the AI Governance in Practice Report 2025 (Consumer Protection Section):

''Standard practices like targeted advertising and recommendations are widely accepted provided they comply with transparency and consent requirements.''

Meanwhile, credit decision-making and misleading AI performance claims (Answers A and B) havealready led to regulatory enforcement.

The AIGP ILT Guide highlights:

''Deceptive claims, biased financial decisions, and unauthorized data use may violate consumer protection and privacy laws. Advertising personalization is routine but must be disclosed appropriately.''

The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such asdeepfakes, misinformation, reputational damage, and erosion of trust.

From theAI Governance in Practice Report 2025:

''With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful.'' (p. 55--56)

Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

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.

Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.

According to theAI Governance in Practice Report 2025, organizations thatdeployAI must engage inperformance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:

''Operational governance... development of: Performance monitoring protocols to ensure systems perform adequately for their intended purposes.'' (p. 12)

''Product governance... includes: System impact assessments to identify and address risk prior to product development or deployment.'' (p. 11)

Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:

''The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance...'' (p. 7)

Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.

Let's now evaluate the incorrect options:

A . Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.

C . A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.

D . Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.

Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.