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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 (Q133-Q138):

NEW QUESTION # 133
During the first month when the company monitors the model for bias, it is most important to?

A. Continue disparity testing.
B. Analyze the quality of the training and testing data.
C. Provide regular awareness training.
D. Document the results of final decisions made by the human underwriter.

Answer: A

Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking for bias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report 2024:
"Performance monitoring protocols... should include mechanisms to assess and measure disparities in outcomes across different demographic groups." (p. 12)
"Bias may not be evident during pre-deployment testing but can emerge in real-world use." (p. 41)
* B. Awareness trainingis helpful, but not a technical bias mitigation activity.
* C. Analyzing training datais apre-deploymenttask.
* D. Documenting human decisionsmay support auditability but doesn't detect bias in AI outputs.

NEW QUESTION # 134
Scenario:
An organization is evaluating different AI models for integration into its internal workflows. Before moving forward with a particular AI solution from a third-party vendor, the governance team needs to assess the ethical and operational implications of the model.
The most important policy to assess the operations of an AI model is to follow the:

A. Security policy of the model provider
B. Privacy policy of the model provider
C. Acceptable use policy of the model provider
D. Code of conduct policy of the model provider

Answer: C

Explanation:
The correct answer is A. The Acceptable Use Policy (AUP) sets the primary terms and conditions under which the AI model can be used, including limitations, prohibited uses, and operational constraints.
From the AIGP ILT Guide:
"The AUP is the most critical document to assess what you can and cannot do with the AI system. It governs use cases, outlines forbidden uses (e.g., disinformation), and supports responsible AI practices." Also confirmed in the AI Governance in Practice Report 2024 (Third-Party AI Assurance section):
"Reviewing and adhering to the acceptable use policy ensures that the model is being used in a manner that aligns with both provider expectations and ethical AI practices."

NEW QUESTION # 135
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?

A. Require the procurement and deployment teams to agree upon the AI tool
B. Continue to require the company's hiring personnel to manually screen all applicants
C. Test the AI tool pre- and post-deployment
D. Ensure the vendor provides indemnification for the AI tool

Answer: C

Explanation:
The correct answer is A. Pre- and post-deployment testing ensures bias, accuracy, and fairness are evaluated and corrected as needed, which is essential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report 2024 (Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).

NEW QUESTION # 136
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. Conducting an assessment of existing compliance programs to determine overlaps and integration points.

Answer: B

Explanation:
Employing a new software platform is a technology solution rather than a foundational compliance strategy step; organizations typically first assess existing programs, ethics, and stakeholder concerns before selecting tools.

NEW QUESTION # 137
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 to apply during the testing?

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

Answer: C

Explanation:
Minimizing human involvement during testing is not a best practice; human oversight is critical to validate model behavior and ensure ethical and accurate performance.

NEW QUESTION # 138
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