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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 (Q192-Q197):

NEW QUESTION # 192
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?

A. The test data should be anonymized to the extent practicable.
B. Testing should be performed specific to the intended uses.
C. Testing should minimize human involvement to the extent practicable.
D. The test data should be representative of the expected operationaldata.

Answer: C

Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.

NEW QUESTION # 193
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 accidental disclosure to its confidential and proprietary information.
C. Avoid using technology that cannot be monetized.
D. Avoid the time necessary to train employees on acceptable use.

Answer: B

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 # 194
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API")developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The agency has taken governance actions such as:
* Conducting an impact assessment
* Providing legal disclosures
* Enabling bias mitigation and explainability
* Complying with regulatory requirements
Which of the following should be included in the marketing company's disclosures about the use of the LLM EXCEPT?

A. Compliance with law
B. Acknowledgement of limitations
C. Intended purpose
D. Proprietary methods

Answer: D

Explanation:
The correct answer isB - Proprietary methods. While transparency is important, organizations arenot obligated to disclose proprietary algorithms, methods, or trade secretsin public disclosures.
From the AIGP Body of Knowledge - Transparency & Disclosures:
"AI system users should disclose the purpose, capabilities, limitations, and applicable legal context-but not sensitive IP." AI Governance in Practice Report2025(Transparency Section) states:
"Disclosure requirements balance public understanding with the need to protect proprietary business interests.
Proprietary training methods are not expected to be disclosed."
Thus, while it's best practice to disclose theintended purpose, legal compliance, and system limitations, internal proprietary techniquesare usually excluded.

NEW QUESTION # 195
CASE STUDY
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions. One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
All of the following are potential negative consequences created by using the AI tool to help make hiring decisions EXCEPT?

A. Privacy violations
B. Automation bias
C. Candidate quality
D. Disparate impacts

Answer: C

Explanation:
The correct answer isB. "Candidate quality" isnota negative consequence of using AI-rather, it is the intendedbenefitof using such tools (e.g., more efficient filtering of strong candidates).
From the AIGP ILT Guide:
"Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring. These risks may arise when AI models replicate biases present in training data or obscure the decision logic." AI Governance in Practice Report 2025 (Bias and Fairness Section) also warns:
"Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups." Candidate quality is agoal, not a risk, makingB the correct answerfor what isnota negative outcome.

NEW QUESTION # 196
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. Addressing specific issues related to managing procurement processes with third parties that provide or develop AI systems for their organization
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. Recommending key activities to assess and manage risk: test, evaluate, verify and validate (TEVV)

Answer: A

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 # 197
......

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