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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
Exam Duration:165 minutes
Certificate Validity Period:2 years
Available Languages:English
Exam Price:USD 799 (non-member) / USD 649 (IAPP member)
Exam Format:Single-select, Multiple-choice, Multi-select, Scenario-based questions
Real Exam Qty:100 (85 scored + ~15 unscored pilot questions)
Passing Score:300 (scaled score out of 100–500)
Related Certifications:CIPP/US
CIPT
CIPP/E
CIPM
Recommended Training:IAPP Official AIGP Training and Resources
AIGP Practice Exam (Official IAPP Store)
Exam Registration:Pearson VUE Scheduling Portal (via IAPP account)
Official IAPP AIGP Exam Registration
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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q10-Q15):

NEW QUESTION # 10
Scenario:
A distributor operating in the EU is responsible for selling imported high-risk AI systems to businesses. The distributor wants to ensure they fulfill all applicable obligations under the EU AI Act.
All of the following are obligations of a distributor of high-risk AI systems under the EU AI Act EXCEPT?

Answer: D

Explanation:
The correct answer is C. Registration in the EU database is an obligation of providers of high-risk AI systems-not distributors.
From the AIGP ILT Guide - Roles & Obligations Module:
"Distributors must verify CE marking, ensure instructions for use are provided, inform authorities of risks, and take corrective action when necessary. However, registration duties in the EU database lie with the provider." Also from the AI Governance in Practice Report 2024:
"The AI Act differentiates responsibilities for developers, providers, importers, and distributors. Only providers of high-risk systems are obligated to register their systems in the EU AI Database." Distributors focus on verification and communication, not formal registration.


NEW QUESTION # 11
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model. Which of the following is NOT a best practice to apply during the testing?

Answer: D

Explanation:
Minimizing human involvement during testing is not a best practice; human oversight is critical to validate model behavior and ensure ethical and accurate performance.


NEW QUESTION # 12
What is the most important reason for documenting risks when developing an AI system?

Answer: B

Explanation:
Documenting risks creates a clear record of identified issues, decisions, and mitigations, which is essential for reducing potential legal and regulatory liability if the AI system later causes harm.


NEW QUESTION # 13
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

Answer: B

Explanation:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference: AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).


NEW QUESTION # 14
Which type of existing assessment could best be leveraged to create an AI impact assessment?

Answer: C

Explanation:
A privacy impact assessment (PIA) is most relevant for creating an AI impact assessment because AI systems often process personal data and pose privacy risks that must be identified and mitigated.


NEW QUESTION # 15
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