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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional
Exam Number:	AIGP
Passing Score:	300
Exam Duration:	150 minutes
Related Certifications:	CIPP/US CIPT CIPP/E CIPP/A CIPM
Exam Format:	Multiple-choice
Real Exam Qty:	100
Certificate Validity Period:	2 Years
Exam Price:	USD 550
Available Languages:	English
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online (OnVUE) or Test Center (Pearson VUE)
Pre Condition:	No specific prerequisites. Recommended background in privacy, compliance, legal, or technology.
Official Syllabus URL:	https://iapp.org/certify/aigp/

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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 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 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q164-Q169):

NEW QUESTION # 164
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact AI system?

A. When the algorithmic impact assessment has been completed.
B. Upon release of a new version of the system.
C. When use of the system causes or is likely to cause material harm.
D. Upon initial deployment of the system.

Answer: C

Explanation:
Under the Canadian Artificial Intelligence and Data Act (AIDA), the responsible party must notify the Minister of Innovation, Science and Industry as soon as feasible if the use of a high-impact AI system results in or is likely to result in material harm.

NEW QUESTION # 165
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers. Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

A. A classic machine learning model.
B. A subscription-based, multimodal model.
C. A free large language model.
D. A proprietary generative AI model.

Answer: A

Explanation:
A classic machine learning model offers full customization, transparency, and portability, allowing the organization to tailor training data, model architecture, and deployment without dependence on a proprietary vendor, thereby avoiding lock-in.

NEW QUESTION # 166
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. Consulting experts to consider the ethical principles underpinning the use of AI within the organization.
B. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
C. Meeting with and obtaining approval from senior management.
D. Employing a new software platform to modernize existing compliance processes across the organization.

Answer: D

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 # 167
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

A. To apply their own judgment to the initial assessment.
B. To provide a reminder of a right to appeal.
C. To ensure they provide transparency to applicants on the model.
D. To solicit on-going feedback on model performance.

Answer: A

Explanation:
Training underwriters ensures they understand the AI's limitations and can apply their own judgment to initial assessments, preventing over-reliance on potentially biased model outputs.

NEW QUESTION # 168
Which of the following compliance related controls within an organization is most easily adapted to identify AI risks?

A. Privacy impact assessments.
B. Transfer risk assessments.
C. Privacy training.
D. Penetration testing.

Answer: A

Explanation:
The correct answer is D because Privacy Impact Assessments are already structured processes designed to identify risks related to data use, processing, and potential harm to individuals. These assessments can be readily adapted to evaluate AI-specific risks, such as bias, automated decision-making impacts, and data protection concerns. AI governance frameworks emphasize leveraging existing compliance mechanisms to efficiently integrate AI risk management without duplicating processes. Privacy impact assessments align closely with AI risk evaluation because they examine how personal data is collected, used, and protected throughout the system lifecycle. Other options, such as penetration testing or training, focus on narrower objectives like security or awareness and are not comprehensive tools for identifying broader AI risks.
Adapting PIAs supports a risk-based, scalable, and governance-aligned approach to managing AI systems.

NEW QUESTION # 169
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