IAPP AIGP Exam Topic | Free AIGP Test Questions

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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 Price:USD 799 (non-member) / USD 649 (IAPP member)
Passing Score:300 (scaled score out of 100–500)
Related Certifications:CIPP/US
CIPM
CIPT
CIPP/E
Exam Duration:165 minutes
Exam Format:Multiple-choice, Scenario-based questions, Multi-select, Single-select
Certificate Validity Period:2 years
Real Exam Qty:100 (85 scored + ~15 unscored pilot questions)
Available Languages:English
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 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 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 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 (Q199-Q204):

NEW QUESTION # 199
Which of the following considerations is the most important in mitigating the potential of bias in training and testing data?

Answer: B

Explanation:
The correct answer is B because ensuring that training and testing data is representative is the most critical factor in mitigating bias in AI systems. AI governance frameworks emphasize that biased or unrepresentative datasets can lead to discriminatory outcomes, particularly when certain demographic groups are underrepresented or overrepresented. Representative data helps ensure that the model performs fairly and accurately across different populations. While privacy-enhancing tools and consent address legal and ethical data use, they do not directly prevent bias in model outcomes. Similarly, assessing the sufficiency of third- party data focuses on quantity rather than fairness or distribution. Effective bias mitigation begins with evaluating whether the dataset reflects the diversity and characteristics of the real-world population the AI system will impact.


NEW QUESTION # 200
What is the most important reason to document the results of AI testing?

Answer: C

Explanation:
Documenting AI testing results creates a verifiable audit trail, which is essential for accountability, regulatory compliance, and demonstrating that risks were properly assessed and mitigated.


NEW QUESTION # 201
What is the most significant risk of deploying an AI model that can create realistic images and videos?

Answer: A

Explanation:
The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such asdeepfakes, misinformation, reputational damage, and erosion of trust.
From theAI Governance in Practice Report2025:
"With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful." (p. 55-56)


NEW QUESTION # 202
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an Al system?

Answer: A

Explanation:
When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing user rights and ethical considerations in AI.


NEW QUESTION # 203
What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model's output?

Answer: A

Explanation:
Differential privacy adds controlled noise to the training process, making it statistically unlikely to identify specific training data from the model's outputs.


NEW QUESTION # 204
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