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

Topic	Details
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 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 3	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 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 (Q89-Q94):

NEW QUESTION # 89
Scenario:
Business A provides grammar and writing assistance tools and licenses a generative AI model from Business B to enhance its offerings. Business A is concerned that the AI model might produce inappropriate or toxic content and wants to implement governance processes to prevent this.
Which of the following governance processes should Business A take tobest protect its usersagainst potentially inappropriate text?

A. Business A should ask Business B for detailed documentation on the generative AI model's training data and whether it contained toxic or obscene sources
B. Business A should establish a user reporting feature that allows users to flag toxic or obscene text, and report any incidents to Business B
C. Business A should test that the AI model performs as expected and meets their minimum requirements for filtering toxic or obscene text
D. Business A should fine-tune the AI model on user-generated text that has been verified to be appropriate

Answer: C

Explanation:
The correct answer isB. According to responsible AI practices,pre-deployment testingto ensurethe model behaves as expected and aligns with organizational requirements is critical.
From the AIGP ILT Guide:
"Testing for unacceptable outcomes such as toxicity, discrimination, or hallucinations should be included in the AI governance life cycle, particularly during development and prior to deployment." Also emphasized in the AI Governance in Practice Report2025:
"Organizations must verify legal and regulatory compliance, monitor performance, and mitigate risksprior to deployment." Testing the model tomeet safety and appropriateness standardsis more proactive and preventive than relying solely on user reporting or requesting documentation.

NEW QUESTION # 90
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for productrecommendations.
What is the most challenging aspect of managing this request?

A. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.
B. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
C. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.
D. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.

Answer: D

Explanation:
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals.
Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

NEW QUESTION # 91
What is the technique to remove the effects of improperly used data from an ML system?

A. Model inversion.
B. Data cleansing.
C. Model disgorgement.
D. Data de-duplication.

Answer: C

Explanation:
Model disgorgement is the technique used to remove the influence of improperly used or problematic data from a trained machine learning model.

NEW QUESTION # 92
A US-based mortgage lender has purchased a chatbot. They plan to have the chatbot collect information from consumers who are interested in loans and offer the consumers 2-3 different options based on its current pricing and product offerings, which change frequently. This chatbot was initially developed and previously deployed by a Russian airline for booking flights.
The best option for the part of the process that generates the loan offers is?

A. Retrieval-Augmented Generation.
B. Multimodal Generative AI.
C. Quantum computing
D. Expert System.

Answer: D

NEW QUESTION # 93
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

A. Explainable and Interpretable.
B. Tested and Effective.
C. Secure and Resilient.
D. Accountable and Transparent.

Answer: A

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.

NEW QUESTION # 94
......

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