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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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 (Q138-Q143):

NEW QUESTION # 138
A UK company has designed a facial recognition model to support border control. The EU AI Act would apply to the model in all of the following situations EXCEPT if:

Answer: B

Explanation:
The EU AI Act applies to AI systems used within the EU or by entities established in the EU.
Deployment of the model solely at UK border checkpoints falls outside the Act's jurisdiction since the UK is no longer an EU member.


NEW QUESTION # 139
What is a primary liability concern when a company deploys a proprietary Agentic AI model?

Answer: C

Explanation:
The correct answer is A because deploying a proprietary agentic AI model places direct responsibility on the organization for the actions and outcomes of that system. AI governance frameworks emphasize accountability as a core principle, meaning organizations must take responsibility for harms caused by their AI systems, including misleading, biased, or unsafe outputs. Agentic AI systems, which can act autonomously and make decisions, introduce heightened liability risks because their behavior may directly impact users or third parties. Regulatory approaches increasingly focus on ensuring that deployers cannot shift responsibility, especially when the system is proprietary and under their control. The other options relate to operational or technical considerations, not primary legal liability. Ensuring accountability for AI behavior is central to compliance, risk management, and maintaining trust.


NEW QUESTION # 140
Which of the following is an obligation of an importer of high risk AI systems under the EU AI Act?

Answer: C

Explanation:
Importers of high-risk AI systems must verify that the system bears the CE marking and that the EU Declaration of Conformity has been issued by the provider. This is a direct obligation under the EU AI Act.


NEW QUESTION # 141
Which of the following is an obligation of an importer of high-risk AI systems under the EU AI Act?

Answer: C

Explanation:
Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenot the parties responsible for affixing the CE marking or providing technical documentation-but they must verify that these have been done by the provider.
From theAI Governance in Practice Report 2024:
"Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed." (p. 34-35) Thus:
* A. Provide technical documentation- done by theprovider.
* B. Affix the CE marking-provider'sresponsibility.
* C. Verify the Declaration of Conformity-importer obligation.
* D. Conduct a DPIA- relevant under data protection laws,not requiredunder the EU AI Act for importers.


NEW QUESTION # 142
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Training an AI model is time-consuming because of?

Answer: A,C,D

Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report 2025:
"Most AI requires sizeable amounts of high-quality data... to ensure desired and accurate output." (p. 15)
"The accuracy of AI model outputs depends significantly on the quality of their inputs." (p. 24)
"Complex AI systems... with many parameters... result in long development and training phases." (p. 32) B . Maturity of governanceaffects oversight, not training time.
D . Number of stakeholdersaffects alignment, not direct training duration.


NEW QUESTION # 143
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