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IAPP AIGP Exam Overview:

Certification Vendor:IAPP
Exam Name:IAPP Certified Artificial Intelligence Governance Professional
Exam Number:AIGP
Exam Price:USD 550
Exam Format:Multiple-choice
Available Languages:English
Real Exam Qty:100
Related Certifications:CIPM
CIPP/A
CIPP/US
CIPP/E
CIPT
Certificate Validity Period:2 Years
Passing Score:300
Exam Duration:150 minutes
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:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q67-Q72):

NEW QUESTION # 67
A company initially intended to use a large data set containing personal information to train an AI model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model. This is an example of applying which privacy-enhancing technique (PET)?

Answer: C

Explanation:
Anonymization permanently removes personal identifiers from data, preventing re-identification and enhancing privacy.


NEW QUESTION # 68
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?

Answer: A

Explanation:
The correct answer isA. Pre- and post-deployment testing ensuresbias, accuracy, and fairnessare evaluated and corrected as needed, which isessential 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 Report2025(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 # 69
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?

Answer: A

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 # 70
Which of the following is a foundational characteristic of effective AI governance?

Answer: A

Explanation:
Effective AI governance fundamentally requires the engagement of a cross-functional team to incorporate diverse perspectives and expertise throughout the AI lifecycle.


NEW QUESTION # 71
AU.S. mortgage company developed an AI platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The AI platform will be used to automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default. Which of the following laws is NOT relevant to this use case?

Answer: A

Explanation:
Title VII of the Civil Rights Act primarily governs employment discrimination and is not directly relevant to mortgage lending decisions, unlike the other listed laws.


NEW QUESTION # 72
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