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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q154-Q159):

NEW QUESTION # 154
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT?

A. Publishing a detailed report on the training data used.
B. Conducting a conformity assessment.
C. Conducting post-market monitoring.
D. Retaining system-generated logs for at least six months.

Answer: A

Explanation:
The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.
From theAI Governance in Practice Report2025:
"It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations." (p. 34)
"To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle."
"Conducting post-market monitoring" and "conformity assessments" areexplicitrequirements for high-risk systems. (p. 34-35) However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.
* A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.

NEW QUESTION # 155
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?

A. The data protection impact assessments carried out on the Al system and legal bases for processing.
B. The existence of automated decision-making and meaningful information on its logic and consequences.
C. The personal data used during processing, including inferences drawn by the Al system about the data.
D. The contact details of the data protection officer and the data protection national authority.

Answer: B

Explanation:
The GDPR's transparency principle requires that when personal data is processed for automated decision- making, including profiling, data subjects must be informed about the existence of such automated decision- making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.

NEW QUESTION # 156
Which of the following is NOT a common type of machine learning?

A. Deep learning.
B. Reinforcement learning.
C. Cognitive learning.
D. Unsupervised learning.

Answer: C

Explanation:
The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.

NEW QUESTION # 157
A shipping service based in the US is looking to expand its operations into the EU. It utilizes an in-house developed multimodal AI model that analyzes all personal data collected from shipping senders and recipients, and optimizes shipping routes and schedules based on this data. As they expand into the EU, all of the following descriptions should be included in the technical documentation for their AI model EXCEPT:

A. A general description of the AI system.
B. A detailed description of the elements of the AI system and of the process for its development.
C. A description of the appropriateness of the performance metrics for the specific AI system.
D. A description of the prioritization of the risks of deployment of the AI system.

Answer: D

Explanation:
While risk management is important, the EU AI Act requires documenting the AI system's description, performance metrics appropriateness, and detailed technical elements, but not specifically the prioritization of deployment risks in the technical documentation.

NEW QUESTION # 158
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?

A. The location(s) where data is stored.
B. The human oversight measures employed.
C. How an individual may contest a decision.
D. The fact that an Al system is being used.

Answer: A

Explanation:
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.

NEW QUESTION # 159
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