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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 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 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 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 (Q65-Q70):

NEW QUESTION # 65
Which of the following use cases would be best served by a non-AI solution?

Answer: A

Explanation:
Developing a social media presence for a non-profit is best served by non-AI solutions. This task primarily involves content creation, community engagement, and strategic planning, which are effectively managed by human expertise and traditional marketing tools. AI is more suitable for tasks requiring automation, large- scale data analysis, and personalized recommendations, such as e-commerce personalization, forecasting cost overruns, or automating customer service responses. Reference: AIGP Body of Knowledge on AI Use Cases and Applications.


NEW QUESTION # 66
To assist its internal recruiters with filtering job applications, a company decides to develop in-house an AI model for screening and ranking job applicants ' resumes.
Which of the following is a unique issue this company might face compared with using a third-party AI service?

Answer: A

Explanation:
Developing an AI model internally creates a direct need for technical expertise covering model development, infrastructure, deployment, monitoring, retraining, security, testing, and maintenance. This makes A the strongest distinguishing issue. Third-party AI services can reduce this operational burden because the external provider supplies much of the model and underlying infrastructure; IAPP guidance specifically notes that third-party APIs can accelerate deployment and reduce the burden of managing complex infrastructure. B is incorrect because an internally developed model generally provides more , not less, technical control. C is not necessarily true because total costs depend on scale, licensing, infrastructure, and usage. D is also too absolute: although developing a system can create substantial provider obligations, responsibility under AI laws and governance frameworks may still be distributed among providers, deployers, vendors, processors, and other actors depending on the circumstances.


NEW QUESTION # 67
Which risk category addresses situations where AI systems behave in ways inconsistent with their intended objectives?

Answer: A


NEW QUESTION # 68
Scenario:
A distributor operating in the EU is responsible for selling imported high-risk AI systems to businesses. The distributor wants to ensure they fulfill all applicable obligations under the EU AI Act.
All of the following are obligations of a distributor of high-risk AI systems under the EU AI Act EXCEPT?

Answer: A

Explanation:
The correct answer isC.Registration in the EU databaseis an obligation ofprovidersof high-risk AI systems- not distributors.
From the AIGP ILT Guide - Roles & Obligations Module:
"Distributors must verify CE marking, ensure instructions for use are provided, inform authorities of risks, and take corrective action when necessary. However, registration duties in the EU database lie with the provider." Also from the AI Governance in Practice Report2025:
"The AI Act differentiates responsibilities for developers, providers, importers, and distributors. Only providers of high-risk systems are obligated to register their systems in the EU AI Database." Distributors focus onverification and communication, not formal registration.


NEW QUESTION # 69
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?

Answer: D

Explanation:
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report 2025:
"Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms." (p. 11, 34) Ais about liability, not auditability.
Bmatters for IP rights, not oversight.
Crelates to lifecycle responsibility but doesn't guarantee audit access.


NEW QUESTION # 70
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