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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q63-Q68):

NEW QUESTION # 63
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
When prioritizing the updates to its policies, rules and procedures to include the new AI system for user authentication, the organization should:

A. Update third-party data sharing policies
B. Ensure that any personal data used is only processed for a specific and lawful purpose
C. Update security controls for sensitive data
D. Reduce the complexity of the policy to make it easier for non-technical employees to understand

Answer: B

Explanation:
The correct answer is C. This action ties directly into principles of data minimization, purpose limitation, and lawfulness of processing, which are central to privacy and AI governance.
From the AIGP Body of Knowledge, Section on Privacy Considerations:
"Personal data must only be processed for specified and lawful purposes. Organizations must consider whether they have a legal basis for processing such data under data protection laws like the GDPR or CCPA." Additionally, AI Governance in Practice Report 2024 emphasizes:
"One of the most significant challenges when designing and developing AI systems is ensuring the data used is appropriate for the intended purpose... Managing unnecessary data, especially data that may contain sensitive attributes, can increase risk."

NEW QUESTION # 64
The best practice to manage third-party risk associated with AI systems is to create and implement policies that?

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

Answer: B

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 Report 2025:
"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 # 65
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC 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 t A. human underwriter for final review.
ABC 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. ABC has 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, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?

A. Detailed terms of service.
B. Customer service support.
C. A transparency notice.
D. An opt-out mechanism.

Answer: C

Explanation:
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.

NEW QUESTION # 66
What is the best reason for a company adopt a policy that prohibits the use of generative Al?

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

Answer: D

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 # 67
Scenario:
A large multinational organization is rolling out a company-wide AI governance initiative. To build awareness and support adoption, they are evaluating different ways to train employees and stakeholders across departments, including legal, technical, marketing, and customer-facing roles.
Which of the following typical approaches is a large organization least likely to use to responsibly train stakeholders on AI terminology, strategy and governance?

A. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact
B. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture
C. Providing role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model
D. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems

Answer: D

Explanation:
The correct answer isA. While educating technical staff is important, expectingall technical employees to be retooled as AI developersis unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should berole-specificand align with the individual's function and responsibilities... Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report 2025 supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus,broad development training for all technical employeesis the least practical and least likely approach.

NEW QUESTION # 68
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