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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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q209-Q214):

NEW QUESTION # 209
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
What is the best strategy to mitigate the bias uncovered in the loan applications?

A. Delete all gender-based data in the data set.
B. Document all instances of bias in the data set.
C. Procure a third-party statistical bias assessment tool.
D. Retrain the model with data that reflects demographic parity.

Answer: D

Explanation:
Retraining the model with data that reflects demographic parity helps reduce bias by balancing representation and mitigating unfair disparities in model outcomes.

NEW QUESTION # 210
Machine learning is best described as a type of algorithm by which?

A. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
B. Systems can automatically improve from experience through predictive patterns.
C. Systems can mimic human intelligence with the goal of replacing humans.
D. Previously unknown properties are discovered in data and used to predict and make improvements in the data.

Answer: B

Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

NEW QUESTION # 211
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 22989andISO/IEC 42001can be valuable resources for AI Governance professionals inall of the following ways EXCEPT:

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

Answer: C

Explanation:
The correct answer isC. ISO/IEC 22989 and 42001 focus onterminology, risk, and management systems, butdo 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 Report2025:
"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 # 212
After initially deploying a third-party AI model, you learn the developer has released a new version.
As deployer of this third-party model, what should you do?

A. Retrain the model.
B. Communicate necessary updates to your users.
C. Audit the model.
D. Seek input from data scientists.

Answer: C

Explanation:
When anew versionof a third-party model is released, the deployer must ensure it still meets safety, performance, and compliance requirements - which calls for aformal audit.
From theAI Governance in Practice Report2025:
"Any updates or changes to AI systems should trigger a re-evaluation to ensure continued compliance and performance." (p. 12)
"Post-market monitoring includes reassessing the impact of updated models or retraining." (p. 35)

NEW QUESTION # 213
What is the best reason for a company adopt a policy that prohibits the use of generative Al?

A. Avoid using technology that cannot be monetized.
B. Avoid the time necessary to train employees on acceptable use.
C. Avoid accidental disclosure to its confidential and proprietary information.
D. Avoid needing to identify and hire qualified resources.

Answer: C

Explanation:
The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company's competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.

NEW QUESTION # 214
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