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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.
Topic 2
  • Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.
Topic 3
  • Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
Topic 4
  • Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.

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AIGP Exam Simulations - AIGP Vce Format

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q63-Q68):

NEW QUESTION # 63
After initially deploying a third-party AI model, you learn the developer has released a new version. As deployer of this third-party model, what should you do?

Answer: A

Explanation:
When a third-party developer releases a new model version, the deployer must first audit the updated model to determine whether the changes introduce new risks, alter performance, or affect compliance obligations before considering deployment or communicating updates.


NEW QUESTION # 64
All of the following are examples of biometric data in the US EXCEPT?

Answer: A

Explanation:
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.


NEW QUESTION # 65
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an Al system?

Answer: A

Explanation:
When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.


NEW QUESTION # 66
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers. Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

Answer: D

Explanation:
A classic machine learning model offers full customization, transparency, and portability, allowing the organization to tailor training data, model architecture, and deployment without dependence on a proprietary vendor, thereby avoiding lock-in.


NEW QUESTION # 67
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?

Answer: C

Explanation:
An AI system'sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.
From theAI Governance in Practice Report 2025:
"There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization's role, footprint, broader risk-governance profile and maturity." (p. 4)
"AI governance starts with defining the corporate strategy for AI... and formulating policy standards and operational procedures to reflect industry, use case, and location." (p. 11) A- Organizational accountability is broader and not directly scoped by industry or function.
C- Diversity of data sources is tied to data strategy.
D- Explainability is more influenced by model type, not use context.


NEW QUESTION # 68
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