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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 (Q190-Q195):

NEW QUESTION # 190
A company launched an AI model last year. One year later, the company was acquired and the entire team that developed, deployed and oversaw the model left. How can the company best maintain the model from a governance perspective going forward?

A. Form an oversight committee responsible for all model decisions.
B. Create a trust center on the company's website with its ethical principles.
C. Hire key employees subject to non-compete clauses.
D. Create a library of all documentation created during the AI lifecycle.

Answer: D

Explanation:
Maintaining comprehensive documentation of the AI lifecycle is essential for governance continuity, enabling new teams to understand, manage, and update the model responsibly.

NEW QUESTION # 191
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Which of the following best describes data dependency with regard to an AI model?

A. The complexity and architecture of an AI model are influenced by the characteristics of its training data.
B. Having large, high-quality datasets reduces data dependency in AI models.
C. Biases and limitations in an AI model often originate from biases and limitations present in its training data.
D. An AI model's performance depends on the quality, quantity, and diversity of its training data.
E. Using privacy-enhancing techniques is a core principle of data dependency in AI models.

Answer: A,C,D

Explanation:
AI models are intrinsically dependent on the data used to train, validate, and test them. NIST specifically identifies AI's inherent dependence on training data and links model reliability to data quality and representativeness. Therefore, A is correct. Training-data characteristics-including modality, structure, features, quantity, and complexity-also influence the appropriate model design and architecture, supporting D. E is correct because biases, gaps, nonrepresentative samples, and other limitations in training data can become embedded in model behavior and subsequently affect outputs. Privacy-enhancing technologies may reduce privacy risks, but they do not define data dependency, eliminating B. Likewise, improving dataset size and quality can improve performance but does not remove the model's fundamental dependence on data, so C is incorrect.

NEW QUESTION # 192
The best practice to manage third-party risk associated with AI systems is to create and implement policies that?

A. Require third-party AI systems to undergo a comprehensive audit by an external cybersecurity firm every six months.
B. Focus on the technical aspects of AI systems, such as data security, while ethical risks are addressed through suitable contracts.
C. Provide for an appropriate level of due diligence and ongoing monitoring based on the defined risk.
D. Focus on the financial stability of third-party vendors as the primary criterion for risk assessment.

Answer: C

Explanation:
Third-party risk management for AI systems should beproportional and risk-based, involvinginitial due diligenceandongoing monitoringthat reflects thelevel of risk posedby the third party's AI system.
From theAI Governance in Practice Report2025:
"Third-party due diligence assessments to identify possible external risk and inform selection." (p. 11)
"Legal due diligence may include verification of the personal data's lawful collection by the data broker, review of contractual obligations..." (p. 19)
* Afocuses too narrowly on financial stability.
* Cis excessive and not scalable or aligned with best practices.
* Dinappropriately separates ethical and technical risks; both must be evaluated holistically.

NEW QUESTION # 193
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. How frequently it will be updated.
D. What business purpose it will serve.

Answer: D

Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report2025:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

NEW QUESTION # 194
Cloud-based deployment of AI often has which of the following advantages?

A. Reducing latency at model inferencing.
B. Increasing transparency.
C. Enhancing privacy.
D. Simplifying scaling up and down.

Answer: D

Explanation:
Cloud-based AI deployment simplifies scaling resources up or down on demand, providing flexibility and efficiency.

NEW QUESTION # 195
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