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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 (Q146-Q151):

NEW QUESTION # 146
A company has developed a proprietary AI model that analyzes consumer online behavior and predicts what prices consumers would be willing to pay for certain products, so that retailers may modify pricing accordingly. To test the model, the company has:
- Performed an impact assessment.
- Conducted repeatability tests.
- Exposed the model to edge cases and potential malicious input.
- Conducted adversarial testing to ID security threats.
- Assessed and mitigated discrimination risks.
Which additional responsible AI principle has the company failed to assess?

A. Data Integrity.
B. Fairness.
C. Transparency.
D. Robustness.

Answer: C

Explanation:
While the company addressed security, fairness, and robustness through testing and risk assessment, transparency, ensuring stakeholders understand how the model makes decisions, has not been explicitly assessed.

NEW QUESTION # 147
The best method to ensure a comprehensive identification of risks for a new AI model is?

A. Integration testing.
B. An environmental scan.
C. Red teaming.
D. An impact assessment.

Answer: D

Explanation:
The most comprehensive way to identify a full range of risks - legal, ethical, operational, and societal - for a new AI model is through aformal impact assessment, such as aData Protection Impact Assessment (DPIA)orAlgorithmic Impact Assessment.
From theAI Governance in Practice Report 2024:
"Risk-based approaches are often distilled into organizational risk management efforts, which put impact assessments at the heart of deciding whether harm can be reduced." (p. 29)
"DPIAs... help organizations identify, analyze and minimize data-related risks and demonstrate accountability." (p. 30)
* A. Environmental scanis too general.
* B. Red teamingis useful for adversarial risk but not broad.
* C. Integration testingfocuses on technical/system compatibility, not overall risk.

NEW QUESTION # 148
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. Code of conduct policy of the model provider
B. Security policy of the model provider
C. Privacy policy of the model provider
D. Acceptable use policy of the model provider

Answer: D

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 # 149
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. Testing should minimize human involvement to the extent practicable.
B. The test data should be representative of the expected operational data.
C. Testing should be performed specific to the intended uses.
D. The test data should be anonymized to the extent practicable.

Answer: A

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 # 150
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third-party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because?

A. It may be better suited for applications requiring customization.
B. It can handle voice commands and is more suitable for phone-based customer support.
C. It is less expensive to run
D. It is built for large-scale, complex dialogues and would be more effective in handling high-volume customer inquiries.

Answer: A

Explanation:
Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs- making it preferable when deep control is needed.
From theAI Governance in Practice Report2025:
"Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies." (p. 39)

NEW QUESTION # 151
......

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