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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 (Q84-Q89):

NEW QUESTION # 84
Your company is developing an AI system for global markets. This system involves personal data processing and some decision making that may affect rights of the individuals. Part of your role as an AI governance professional is to ensure the AI system complies with various international regulatory requirements. Which of the following approaches best aligns with a comprehensive understanding of and compliance with AI regulatory requirements in this scenario?

A. Focus on the EU AI Act as it is the most comprehensive regulation and its likely compliance with the act will ensure compliance with other international regulations that apply to AI.
B. Develop a compliance strategy based on the strictest requirements in various regulations, including the EU AI Act, GDPR and HIPAA, and harmonize into a unified compliance framework.
C. Consider that if the AI system complies with local regulations of the country where the company is based, it is not necessary to comply with international regulations as AI is adaptable globally.
D. Adopt a region-specific compliance strategy where the AI is modified to meet regulatory requirements of each region independently, disregarding commonalities or overlaps in international regulations.

Answer: B

NEW QUESTION # 85
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT?

A. Autonomy
B. Adaptability
C. Automation
D. Speed and scale
E. Superintelligence

Answer: E

Explanation:
The correct answer is E - Superintelligence.
While the other options (Autonomy, Automation, Adaptability, Speed and Scale) are commonly cited as real- world characteristics that affect governance today, superintelligence remains a theoretical concept.
From the AIGP ILT Guide and AI Governance in Practice Report 2024:
"Core AI characteristics-such as automation, adaptability, speed, and autonomy-require active governance due to their impact on decision-making, legal liability, and risk." However, superintelligence is speculative and not a current feature of AI systems under governance frameworks like the EU AI Act or NIST RMF.
Thus, it's not a current characteristic requiring governance in real-world enterprise settings.

NEW QUESTION # 86
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria such as a confidence score below a threshold.
To date, the healthcare network has:
Defined its AI ethical principles
Conducted discovery to identify the intended uses and success criteria for the system Established an AI risk committee Assembled a cross-functional team with clear roles and responsibilities Created policies and procedures to document standards, workflows, timelines and risk thresholds during the project The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network ' s existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the AI solution?

A. Engage a third party to perform an audit.
B. Create a bias bounty program.
C. Perform an impact assessment.
D. Procure more data from clinical research partners.

Answer: C

Explanation:
The correct answer is C because performing an impact assessment is the most effective proactive measure to identify and mitigate discrimination risks before model training and testing. AI governance frameworks emphasize early-stage risk identification, particularly for high-stakes domains like healthcare, where bias can lead to harmful or inequitable outcomes. Impact assessments, such as algorithmic or data protection impact assessments, evaluate potential harms, affected populations, and fairness risks prior to development. This enables organizations to design appropriate safeguards, adjust data selection, and implement mitigation strategies before biases become embedded in the model. Options like audits and bias bounty programs are reactive or post-development controls, while simply acquiring more data does not guarantee reduced bias without proper analysis. A structured impact assessment aligns with risk-based governance and supports fairness, accountability, and regulatory compliance.

NEW QUESTION # 87
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. Employing a new software platform to modernize existing compliance processes across the organization.
B. Consulting experts to consider the ethical principles underpinning the use of AI within the organization.
C. Meeting with and obtaining approval from senior management.
D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.

Answer: A

Explanation:
Modernizing compliance software is not a core or necessary activity when designing an integrated responsible AI compliance strategy. It may support operations later, but it is not a typical or essential early step compared to governance approval, stakeholder input, and ethical consultation.

NEW QUESTION # 88
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A. Perform a readiness assessment.
B. Identify known edge cases to monitor post-deployment.
C. Document maintenance teams and processes.
D. Define a model-validation methodology.

Answer: A

Explanation:
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

NEW QUESTION # 89
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