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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 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 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 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 (Q69-Q74):

NEW QUESTION # 69
Scenario:
An organization is building a compliance program to ensure responsible AI deployment. It aims to align operations with AI risk frameworks and mitigate legal, ethical, and operational risks, while still promoting innovation.
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible AI?

A. Launching a survey to understand the concerns and interests of potentially impacted stakeholders
B. Employing a new software platform to modernize existing compliance processes across the organization
C. Consulting experts to consider the ethical principles underpinning the use of AI within the organization
D. Meeting with and obtaining approval from senior management

Answer: B

Explanation:
The correct answer is D. While modernization through software may support efficiency, it is not a foundational or essential component of designing an integrated strategy.
From the AI Governance in Practice Report 2024:
"Integrated strategies rely on senior management support, ethical reviews, and stakeholder engagement... The use of tools and platforms may come later as an operational enhancement." Also confirmed in AIGP Body of Knowledge:
"Key components of a governance framework include leadership buy-in, ethical analysis, and stakeholder input. Tools are supporting elements-not strategic drivers."

NEW QUESTION # 70
During the planning and design phases of the Al development life cycle, bias can be reduced by all of the following EXCEPT?

A. Feature selection.
B. Stakeholder involvement.
C. Data collection.
D. Human oversight.

Answer: A

Explanation:
Bias in AI can be reduced during the planning and design phases through stakeholder involvement, human oversight, and careful data collection. While feature selection is critical in the development phase, it does not specifically occur during planning and design. Ensuring diverse stakeholder involvement and human oversight helps identify and mitigate potential biases early, and data collection ensures a representative dataset. Reference: AIGP Body of Knowledge on AI Development Lifecycle and Bias Mitigation.

NEW QUESTION # 71
An artist has been using an Al tool to create digital art and would like to ensure that it has copyright protection in the United States.
Which of the following is most likely to enable the artist to receive copyright protection?

A. Update the images in a creative way to demonstrate that it is the artist's.
B. Ensure the tool was trained using publicly available content.
C. Provide a log of the prompts the artist used to generate the images.
D. Obtain a representation from the Al provider on how the tool works.

Answer: A

Explanation:
For the artist to receive copyright protection, the most effective approach is to demonstrate that the final artwork includes sufficient creative input by the artist. By updating or altering the images in a way that reflects the artist's personal creativity, the artist can claim originality, which is a core requirement for copyright protection under U.S. law. The other options do not directly address the originality and creative input required for copyright. This is highlighted in the sections on copyright protection in the IAPP AIGP Body of Knowledge.

NEW QUESTION # 72
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

A. To empower and train appropriate cross-functional teams.
B. To establish diverse, equitable and inclusive processes.
C. To determine responsibility for allocating budgetary resources.
D. To enable and encourage participation by external stakeholders.

Answer: A

Explanation:
The NIST AI Risk Management Framework's Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross- functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization's AI principles and ethical standards. Reference:
NIST AI Risk Management Framework documentation, Govern function section.

NEW QUESTION # 73
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. Proprietary methods
B. Intended purpose
C. Acknowledgement of limitations
D. Compliance with law

Answer: A

Explanation:
The correct answer is B - Proprietary methods. While transparency is important, organizations are not obligated to disclose proprietary algorithms, methods, or trade secrets in 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 Report 2024 (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 the intended purpose, legal compliance, and system limitations, internal proprietary techniques are usually excluded.

NEW QUESTION # 74
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