IAPP Updated AIGP Exam Questions and Answers by alys

Once a company moves from being adeployerto also acting as aprovider or developer, it assumesnew obligationsunder regulations like the EU AI Act. One of the core requirements for providers is to produce and maintaintechnical documentation, including descriptions of the model, associated risks, and mitigation strategies.

From theAI Governance in Practice Report 2025:

“Providers of high-risk AI systems must draw up technical documentation demonstrating the system’s conformity with the requirements... including potential risks and safeguards applied.” (p. 34)

“This documentation must be available before placing the system on the market.” (p. 35)

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.”

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.

After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.