Free Practice Questions for IAPP AIGP Exam

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.

The correct answer is B because ISO/IEC 42005 emphasizes integrating AI risk management into existing enterprise risk management frameworks rather than creating siloed or duplicative processes. Effective AI governance requires embedding AI-specific risks into established governance structures such as risk registers, controls, and monitoring systems. This ensures consistency, scalability, and alignment with organizational risk practices. Options C and D introduce parallel or fragmented processes, which contradict the principle of integrated governance and may create inefficiencies or gaps. Option A focuses only on data collection and does not address governance integration. The AI Governance in Practice Report highlights that organizations should incorporate AI risks into broader enterprise risk management and maintain unified risk registers to ensure visibility, accountability, and coordinated mitigation across the organization.

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.

===========

Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive abilities. For the self-driving car to be classified as " strong " AI, it would need to possess full human cognitive abilities to make independent decisions beyond pre-programmed instructions. Reference: AIGP BODY OF KNOWLEDGE and AI classifications.

An AI model is best defined as a program that has been trained on a set of data to find patterns within that data. This definition captures the essence of machine learning, where the model learns from the data to make predictions or decisions. Reference: AIGP BODY OF KNOWLEDGE, which provides a detailed explanation of AI models and their training processes​​.

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.

The correct answer isD.Emotion recognition in the workplaceis flagged asunacceptable or highly restrictedunder the EU AI Act due to its intrusive nature and potential for misuse.

From the AIGP ILT Guide – EU AI Act Training Module:

“AI systems that monitor individuals’ emotions in the workplace or educational settings are listed among prohibited or strictly limited practices under Article 5.”

AI Governance in Practice Report 2025 supports this interpretation:

“Emotion recognition systems, especially in sensitive contexts such as employment or education, raise significant concerns under EU fundamental rights law and are likely to be restricted.”

Other uses listed—such as emergency response or emotion detection in healthcare—may fall underlawful and beneficial uses, especially whenjustified by public interest.

===========

The primary reason the EU is considering updates to its Product Liability Directive is to address digital services and connected products. The current directive does not adequately cover the complexities and challenges posed by modern digital and connected technologies. By updating the directive, the EU aims to ensure that it remains relevant and effective in addressing the liabilities associated with these advanced products, ensuring consumer protection and fair market practices in the digital age.

A greedy algorithm is one that makes the best choice at each step to achieve an immediate objective, without considering the longer-term consequences. It focuses on local optimization at each decision point with the hope that these local solutions will lead to an optimal global solution. However, greedy algorithms do not always produce the best overall solution for certain problems, but they are useful when an immediate, locally optimal solution is desired. Reference: AIGP Body of Knowledge, algorithm types section.