100% Pass 2026 IAPP AIGP: Latest IAPP Certified Artificial Intelligence Governance Professional Learning Materials

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 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 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 4	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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q90-Q95):

NEW QUESTION # 90
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. Whether its training data is disclosed.
B. Whether its system card identifies risks.
C. What business purpose it will serve.
D. How frequently it will be updated.

Answer: C

Explanation:
The primary consideration in selecting a proprietary AI model is whether it effectively serves the intended business purpose.

NEW QUESTION # 91
All of the following apply to enforcement of the EU AI Act EXCEPT:

A. Fines for violations involving banned AI applications may be up to €35 million or 7% global annual turnover (whichever is higher).
B. Rules on General Purpose AI will be enforced 6 months after the Act comes into force.
C. Fines for SMEs and startups will be proportionally capped.
D. Warnings may be imposed for violations rather than fines.

Answer: B

Explanation:
Under the EU AI Act, the enforcement timeline for different provisions varies. While certain rules on prohibited AI practices took effect on 2 February 2025, the rules for General Purpose AI (GPAI) models are set to be enforced 12 months after the Act's entry into force, which is 2 August
2025.

NEW QUESTION # 92
Who is responsible for ongoing maintenance and monitoring of an AI system after it has been put into production?

A. Regulator.
B. Deployer.
C. Developer.
D. User.

Answer: B

Explanation:
The deployer is primarily responsible for operational oversight once an AI system has been placed into use within its organization. This includes monitoring performance, identifying drift or unexpected behavior, following the provider's instructions, escalating incidents, and ensuring that the system continues to operate within approved risk tolerances. Under Article 26 of the EU AI Act, deployers of high-risk AI systems are specifically required to monitor system operation based on the instructions for use and to inform the provider and relevant authorities when serious risks or incidents arise. Developers and providers remain responsible for important lifecycle activities such as design, documentation, updates, and technical support, but ongoing monitoring in the actual deployment environment rests principally with the deployer. A regulator provides external oversight, while an ordinary user does not carry organizational lifecycle responsibility.

NEW QUESTION # 93
Retraining an LLM can be necessary for all of the following reasons EXCEPT:

A. To adjust the model's hyperparameters to a specific use case.
B. To account for new interpretations of the same data.
C. To minimize degradation in prediction accuracy due to changes in data.
D. To ensure interpretability of the model's predictions.

Answer: D

Explanation:
Retraining focuses on adapting the model to data changes or specific use cases, but it does not directly ensure interpretability of predictions, which involves different methods.

NEW QUESTION # 94
Scenario:
A mid-sized tech firm is building its AI governance program and is exploring ISO/IEC standards that could support consistency in terminology and risk assessment processes across teams.
ISO/IEC 22989 and ISO/IEC 42001 can be valuable resources for AI Governance professionals in all of the following ways EXCEPT:

A. Recommending key activities to assess and manage risk: test, evaluate, verify and validate (TEVV)
B. Being applicable to organizations of any size and industry seeking to use AI responsibly and effectively in their design processes, information systems and controls
C. Establishing terminology and describing concepts so that governance team members can communicate with diverse parties and stakeholders from around the world
D. Addressing specific issues related to managing procurement processes with third parties that provide or develop AI systems for their organization

Answer: D

Explanation:
The correct answer is C. ISO/IEC 22989 and 42001 focus on terminology, risk, and management systems, but do not specifically address procurement-related concerns with third-party vendors.
From the AIGP Body of Knowledge - Standards Section:
"ISO/IEC 22989 defines terminology and foundational concepts. ISO/IEC 42001 provides a management system standard for AI. They are not procurement-focused documents." Also confirmed in the AI Governance in Practice Report 2024:
"These standards help establish common language and risk governance procedures. Procurement governance typically falls under separate frameworks or sector-specific guidance." Thus, procurement governance (Option C) is not a central use case for these standards.

NEW QUESTION # 95
......

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