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IAPP AIGP Exam Overview:

Certification Vendor:IAPP (International Association of Privacy Professionals)
Exam Name:IAPP Certified Artificial Intelligence Governance Professional Exam
Exam Number:AIGP
Exam Price:USD 649 (members) / USD 799 (non-members)
Available Languages:English
Related Certifications:CIPM
CIPP
CIPT
Real Exam Qty:100
Passing Score:300 (scaled score out of 500)
Certificate Validity Period:2 years
Exam Duration:165 (including 15-minute optional break)
Exam Format:Multiple-choice, Scenario-based
Recommended Training:Official AIGP Body of Knowledge & Study Guide
IAPP Training & Resources
Exam Registration:IAPP Official Registration
Pearson VUE Scheduling
Sample Questions:IAPP AIGP Sample Questions
Exam Way:Online remote proctored or in-person at Pearson VUE test centers
Pre Condition:No formal prerequisites; open to all professionals
Official Syllabus URL:https://iapp.org/certify/aigp

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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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q205-Q210):

NEW QUESTION # 205
Scenario:
A global organization wants to align with international frameworks on AI governance. They are reviewing guidance from the OECD on how to incorporate broader governance tools into their AI program.
Codes of conductandcollective agreementsare what type of assessment tools as defined by theOrganization for Economic Cooperation and Development (OECD)?

Answer: A

Explanation:
The correct answer isB - Procedural. TheOECD Framework for Classifying AI Systemscategorizescodes of conduct and collective agreementsasprocedural toolsbecause they guide internal governance and decision- making processes.
From the AIGP ILT Participant Guide - Global Governance Models:
"Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement." AI Governance in Practice Report2025elaborates:
"These procedural tools support internal accountability mechanisms and ethics compliance frameworks...
they are part of soft governance."
These toolsdo not measure or analyze technical performance, hence they arenot technical or analytic.


NEW QUESTION # 206
You are an engineer that developed an AI-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool ' s effectiveness?

Answer: D

Explanation:
The correct answer is A because evaluating the effectiveness of an AI system requires measuring how well its outputs align with real-world outcomes. In this case, comparing predicted ad engagement with actual user clicks provides a direct measure of model performance and business impact. AI governance frameworks emphasize outcome-based monitoring to ensure systems meet their intended objectives and deliver value.
Tracking output performance also supports continuous improvement by identifying gaps between predictions and reality. Options B and C focus on indirect indicators, such as internal model behavior or input targeting, which do not fully capture effectiveness. Option D relates to infrastructure performance rather than model outcomes. Monitoring output accuracy and real-world results is essential for validating AI system effectiveness and ensuring accountability.


NEW QUESTION # 207
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

Answer: A

Explanation:
GDPR requires effective controls like human-in-the-loop procedures to prevent decisions based solely on automated processing without human intervention.


NEW QUESTION # 208
The use of paid generative AI public tools is appealing because? (Choose three.)

Answer: C,D,E

Explanation:
Paid generative AI tools are appealing because they are convenient to adopt, often include enhanced privacy and security controls, and receive frequent feature updates.


NEW QUESTION # 209
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: C

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 # 210
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