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IAPP AIGP Exam Overview:
| Certification Vendor: | IAPP (International Association of Privacy Professionals) |
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| Exam Name: | IAPP Certified Artificial Intelligence Governance Professional Exam |
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| Exam Number: | AIGP |
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| Real Exam Qty: | 100 |
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| Exam Format: | Multiple-choice, Scenario-based |
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| Exam Price: | USD 649 (members) / USD 799 (non-members) |
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| Available Languages: | English |
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| Related Certifications: | CIPP CIPT CIPM |
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| Certificate Validity Period: | 2 years |
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| Passing Score: | 300 (scaled score out of 500) |
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| Exam Duration: | 165 (including 15-minute optional break) |
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| Recommended Training: | IAPP Training & Resources Official AIGP Body of Knowledge & Study Guide |
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| Exam Registration: | Pearson VUE Scheduling IAPP Official Registration |
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| Sample Questions: | IAPP AIGP Sample Questions |
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| Exam Way: | Online remote proctored or in-person at Pearson VUE test centers |
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| Pre Condition: | No formal prerequisites; open to all professionals |
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| Official Syllabus URL: | https://iapp.org/certify/aigp |
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| Topic | Details |
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| 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.
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| Topic 2 | - 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.
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| 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.
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| Topic 4 | - 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 modelβs operational life.
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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q213-Q218):
NEW QUESTION # 213
All of the following may be copyright risks from teachers using generative AI to create course content EXCEPT?
- A. Generative AI is generally trained using intellectual property owned by third parties.
- B. Students must expressly consent to this use of generative AI.
- C. Generative AI often creates content without attribution.
- D. Content created by an LLM may be protectable under U.S. intellectual property law.
Answer: B
Explanation:
The correct answer is C. This option concerns consent, which is generally a privacy, transparency, or institutional governance issue rather than a copyright issue. Copyright risk in AI governance usually relates to whether protected third party works were used in model training, whether generated outputs may reproduce copyrighted expression, and whether content is created without proper attribution or clear ownership boundaries. That makes options A, B, and D directly relevant to copyright and intellectual property concerns. In contrast, whether students must expressly consent deals with notice, fairness, and lawful or ethical use in an educational environment. For AIGP exam purposes, it is important to separate intellectual property risks from privacy and data protection obligations, even when both appear in the same classroom or organizational AI use case.
NEW QUESTION # 214
What is the most important factor when deciding whether or not to select a proprietary AI model?
- A. Whether its training data is disclosed.
- B. What business purpose it will serve.
- C. Whether its system card identifies risks.
- D. How frequently it will be updated.
Answer: B
Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.
NEW QUESTION # 215
A company has developed a proprietary AI model that analyzes consumer online behavior and predicts what prices consumers would be willing to pay for certain products, so that retailers may modify pricing accordingly. To test the model, the company has:
- Performed an impact assessment.
- Conducted repeatability tests.
- Exposed the model to edge cases and potential malicious input.
- Conducted adversarial testing to ID security threats.
- Assessed and mitigated discrimination risks.
Which additional responsible AI principle has the company failed to assess?
- A. Transparency.
- B. Fairness.
- C. Data Integrity.
- D. Robustness.
Answer: A
Explanation:
While the company addressed security, fairness, and robustness through testing and risk assessment, transparency, ensuring stakeholders understand how the model makes decisions, has not been explicitly assessed.
NEW QUESTION # 216
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT?
- A. Conducting a conformity assessment.
- B. Retaining system-generated logs for at least six months.
- C. Publishing a detailed report on the training data used.
- D. Conducting post-market monitoring.
Answer: C
Explanation:
The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.
From theAI Governance in Practice Report 2024:
"It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations." (p. 34)
"To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle."
"Conducting post-market monitoring" and "conformity assessments" areexplicitrequirements for high-risk systems. (p. 34-35) However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.
* A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.
NEW QUESTION # 217
Scenario:
An enterprise is evaluating multiple third-party generative AI tools to integrate into its platform. As part of its AI governance policy, it is assessing themost effective methodsto reduce risks related to bias, data misuse, and liability when using third-party solutions.
All of the following are commonly adopted processes and policies in reducing potential risks introduced by third-party AI tools or applications EXCEPT:
- A. Requiring an independent third-party bias audit for third-party generative AI tools
- B. Allowing publicly available information and personally identifiable information (PII) to be incorporated into the prompt
- C. Requiring new use cases of the generative AI tools or applications to be reviewed and approved by the generative AI governance body
- D. Including clauses in the procurement agreement for buyers of generative AI tools to put certain liabilities on the tool supplier
Answer: B
Explanation:
The correct answer isB. AllowingPIIto be freely entered into prompts without safeguards is considered amajor privacy and security riskand is not a responsible governance practice.
From the AIGP ILT Guide - Generative AI & Third-Party Risk Management:
"Use of personal or sensitive information in AI prompts can result in unintended exposure, regulatory breaches, and downstream liability." The AI Governance in Practice Report2025highlights:
"PII should be minimized or protected by design. Prompt engineering should prevent entry of personally identifiable data unless legally and technically safeguarded." A, C, and D are established best practices under responsible AI procurement and use.
NEW QUESTION # 218
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