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IAPP AIGP certifications are thought to be the best way to get good jobs in the high-demanding market. There is a large range of AIGP certifications that can help you improve your professional worth and make your dreams come true. Our IAPP Certified Artificial Intelligence Governance Professional AIGP Certification Practice materials provide you with a wonderful opportunity to get your dream certification with confidence and ensure your success by your first attempt.

IAPP AIGP Exam Overview:

Certification Vendor:IAPP
Exam Name:IAPP Certified Artificial Intelligence Governance Professional
Exam Number:AIGP
Available Languages:English
Exam Price:USD 550
Exam Duration:150 minutes
Real Exam Qty:100
Related Certifications:CIPP/US
CIPP/A
CIPM
CIPP/E
CIPT
Certificate Validity Period:2 Years
Passing Score:300
Exam Format:Multiple-choice
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 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 (Q166-Q171):

NEW QUESTION # 166
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?

Answer: B

Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).


NEW QUESTION # 167
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 # 168
A company that deploys AI but is not currently a provider or developer intends to develop and market its own AI system.
Which obligation would then be likely to apply?

Answer: A

Explanation:
Once a company moves from being adeployerto also acting as aprovider or developer, it assumesnew obligationsunder regulations like the EU AI Act. One of the core requirements for providers is to produce and maintaintechnical documentation, including descriptions of the model, associated risks, and mitigation strategies.
From theAI Governance in Practice Report 2024:
"Providers of high-risk AI systems must draw up technical documentation demonstrating the system's conformity with the requirements... including potential risks and safeguards applied." (p. 34)
"This documentation must be available before placing the system on the market." (p. 35)


NEW QUESTION # 169
After initially deploying a third-party AI model, you learn the developer has released a new version.
As deployer of this third-party model, what should you do?

Answer: C

Explanation:
When anew versionof a third-party model is released, the deployer must ensure it still meets safety, performance, and compliance requirements - which calls for aformal audit.
From theAI Governance in Practice Report2025:
"Any updates or changes to AI systems should trigger a re-evaluation to ensure continued compliance and performance." (p. 12)
"Post-market monitoring includes reassessing the impact of updated models or retraining." (p. 35)


NEW QUESTION # 170
Training data is best defined as a subset of data that is used to?

Answer: B

Explanation:
Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.


NEW QUESTION # 171
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