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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q109-Q114):

NEW QUESTION # 109
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

Answer: A

Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.


NEW QUESTION # 110
Which of the following use cases would be best served by a non-AI solution?

Answer: A

Explanation:
Developing a social media presence for a non-profit is best served by non-AI solutions. This task primarily involves content creation, community engagement, and strategic planning, which are effectively managed by human expertise and traditional marketing tools. AI is more suitable for tasks requiring automation, large-scale data analysis, and personalized recommendations, such as e-commerce personalization, forecasting cost overruns, or automating customer service responses. Reference: AIGP Body of Knowledge on AI Use Cases and Applications.


NEW QUESTION # 111
During the first month when the company monitors the model for bias, it is most important to?

Answer: A

Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking forbias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report2025:
"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 # 112
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT: (Choose three.)

Answer: B,D,E

Explanation:
The commonly recognized unique characteristics of AI that drive the need for comprehensive governance are autonomy and adaptability.
Automation, speed and scale, and superintelligence are not considered core unique characteristics for governance purposes in standard AI governance frameworks, making them the correct exceptions.


NEW QUESTION # 113
A bank is aiming to comply with ISO/IEC 42005:2025, and is studying how to adopt the standard in light of a new AI customer service system that it would like to implement.
In addition to the risk management process the bank already has in place to assess the risks of any potential new systems, which of the following actions is the most effective in adopting the ISO/IEC 42005:2025 standard?

Answer: A

Explanation:
The correct answer is B because ISO/IEC 42005 emphasizes integrating AI risk management into existing enterprise risk management frameworks rather than creating siloed or duplicative processes. Effective AI governance requires embedding AI-specific risks into established governance structures such as risk registers, controls, and monitoring systems. This ensures consistency, scalability, and alignment with organizational risk practices. Options C and D introduce parallel or fragmented processes, which contradict the principle of integrated governance and may create inefficiencies or gaps. Option A focuses only on data collection and does not address governance integration. The AI Governance in Practice Report highlights that organizations should incorporate AI risks into broader enterprise risk management and maintain unified risk registers to ensure visibility, accountability, and coordinated mitigation across the organization.


NEW QUESTION # 114
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