Free Practice Questions for IAPP AIGP Exam

Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.

According to definitions and discussions from theAI Governance in Practice Report2025and U.S. privacy frameworks:

“Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition.” (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.)

Here’s how the options relate:

A. Iris scans– These are physical biometric identifiers.

B. Walking gait– Behavioral biometric used increasingly in surveillance and identification.

C. Keystroke dynamics– Behavioral biometric based on typing patterns.

D. GPS location of a user's fitness watch– This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.

The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs— making it preferable when deep control is needed.

From theAI Governance in Practice Report2025:

“Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies.” (p. 39)

If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as explainable. Explainability in AI refers to the ability to interpret and understand the decision-making process of the AI system. This involves being able to articulate the factors and logic that led to a particular output or decision. Explainability is critical for building trust, enabling users to understand and validate the AI system's actions, and ensuring compliance with ethical and regulatory standards. It also facilitates debugging and improving the system by providing insights into its behavior.

The correct answer isA. TheAcceptable Use Policy (AUP)sets the primary terms and conditions under which the AI model can be used, including limitations, prohibited uses, and operational constraints.

From the AIGP ILT Guide:

“The AUP is the most critical document to assess what you can and cannot do with the AI system. It governs use cases, outlines forbidden uses (e.g., disinformation), and supports responsible AI practices.”

Also confirmed in the AI Governance in Practice Report2025(Third-Party AI Assurance section):

“Reviewing and adhering to the acceptable use policy ensures that the model is being used in a manner that aligns with both provider expectations and ethical AI practices.”

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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.

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.

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.

The White House Blueprint for an AI Bill of Rights focuses on protecting civil rights, privacy, and ensuring AI systems are safe and effective. It includes principles like data privacy (D), human alternatives (A), and safe and effective systems (C). However, it does not specifically address high-risk mitigation standards as a distinct category (B).