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IAPP AIGP Exam Overview:

Certification Vendor:IAPP (International Association of Privacy Professionals)
Exam Name:IAPP Certified Artificial Intelligence Governance Professional (AIGP) Exam
Exam Number:AIGP
Certificate Validity Period:2 years
Exam Duration:165 minutes
Available Languages:English
Real Exam Qty:100 (85 scored + ~15 unscored pilot questions)
Related Certifications:CIPP/E
CIPP/US
CIPM
CIPT
Exam Price:USD 799 (non-member) / USD 649 (IAPP member)
Passing Score:300 (scaled score out of 100–500)
Exam Format:Single-select, Multiple-choice, Multi-select, Scenario-based questions
Recommended Training:AIGP Practice Exam (Official IAPP Store)
IAPP Official AIGP Training and Resources
Exam Registration:Official IAPP AIGP Exam Registration
Pearson VUE Scheduling Portal (via IAPP account)
Sample Questions:IAPP AIGP Sample Questions
Exam Way:Computer-based exam delivered via Pearson VUE (test center or online proctored OnVUE)
Pre Condition:None
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 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q49-Q54):

NEW QUESTION # 49
An AI system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as:

Answer: C

Explanation:
Robustness describes an AI system's ability to maintain performance despite variations or adversarial conditions in the environment.


NEW QUESTION # 50
CASE STUDY
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
All of the following are potential negative consequences created by using the AI tool to help make hiring decisions EXCEPT?

Answer: D

Explanation:
The correct answer isB. "Candidate quality" isnota negative consequence of using AI-rather, it is the intendedbenefitof using such tools (e.g., more efficient filtering of strong candidates).
From the AIGP ILT Guide:
"Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring.
These risks may arise when AI models replicate biases present in training data or obscure the decision logic." AI Governance in Practice Report2025(Bias and Fairness Section) also warns:
"Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups." Candidate quality is agoal, not a risk, makingB the correct answerfor what isnota negative outcome.


NEW QUESTION # 51
What are the roles and responsibilities of deployers of a proprietary model? (Choose three.)

Answer: A,B,C

Explanation:
Deployers of a proprietary model are responsible for ethical testing, ensuring technical performance, and maintaining regulatory compliance when using the model in their environment and for their use case.


NEW QUESTION # 52
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: D

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 # 53
CASE STUDY
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The organization continues planning the adoption of an AI tool to support hiring, but is concerned about potential bias in content generated by AI systems and how that could affect public perception.
Which of the following measures should the company adopt tobest mitigate its risk of reputational harmfrom using the AI tool?

Answer: D

Explanation:
Note:This is the same scenario and question as Question 21 and thus has thesame correct answer: A. It's possible this was duplicated in your original input.
Repeated for clarity:
"Testing AI tools pre- and post-deployment helps ensure they perform as expected and do not introduce bias, privacy issues, or fairness concerns. This mitigates reputational and legal risk." TheAI Governance in Practice Report2025further reinforces:
"Ongoing monitoring and testing post-deployment allows organizations to catch and correct unintended impacts... especially important in HR and hiring contexts."


NEW QUESTION # 54
......

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