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

Topic	Details
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 (Q68-Q73):

NEW QUESTION # 68
All of the following may be permissible uses of an Al system under the EU Al Act EXCEPT?

A. To detect an individual's intent for law enforcement purposes.
B. To promote equitable distribution of welfare benefits.
C. To implement social scoring.
D. To manage border control.

Answer: C

Explanation:
The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual's intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.

NEW QUESTION # 69
A company plans on procuring a tool from an Al provider for its employees to use for certain business purposes.
Which contractual provision would best protect the company's intellectual property in the tool, including training and testing data?

A. The provider willgive privacy notice to individuals before using their personal data to train or test the tool.
B. The provider willwarrant that the tool will work as intended.
C. The provider willobtain and maintain insurance to cover potential claims.
D. The provider willdefend and indemnify the company against infringement claims.

Answer: D

Explanation:
To protect the company's intellectual property, the most pertinent contractual provision is ensuring that the AI provider will defend and indemnify the company against infringement claims. This clause means the provider will take responsibility for any intellectual property disputes that arise, thereby safeguarding the company from potential legal and financial repercussions related to the use of the tool. Other options, while beneficial, do not directly address the protection of intellectual property. This concept is detailed in the contractual best practices section of the IAPP AIGP Body of Knowledge.

NEW QUESTION # 70
What is the technique to remove the effects of improperly used data from an ML system?

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

Answer: B

Explanation:
The correct answer is D, model disgorgement. This technique refers to removing or eliminating the influence of improperly obtained, biased, or unlawfully used data from a trained machine learning model. It is increasingly discussed in AI governance and regulatory enforcement, particularly where models have been trained on data collected without proper consent or in violation of legal requirements. Instead of merely cleaning datasets, model disgorgement may require retraining or discarding the model entirely to ensure that problematic data no longer influences outputs. This aligns with accountability and compliance principles in AI governance, where organizations must ensure lawful data use throughout the AI lifecycle. Other options like data cleansing and de-duplication address data quality but do not fully remove learned patterns already embedded in trained models.

NEW QUESTION # 71
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

A. To empower and train appropriate cross-functional teams.
B. To enable and encourage participation by external stakeholders.
C. To establish diverse, equitable and inclusive processes.
D. To determine responsibility for allocating budgetary resources.

Answer: A

Explanation:
The NIST AI Risk Management Framework's Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross- functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization's AI principles and ethical standards. Reference:
NIST AI Risk Management Framework documentation, Govern function section.

NEW QUESTION # 72
CASE STUDY
Please use the following to answer the next question:
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address AI governance.
The marketing company has:
- Entered into a contract with the technology company with suitable
representations and warranties.
- Completed an impact assessment on the LLM for this intended use.
- Built technical guidance on how to measure and mitigate bias in the
LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Followed applicable regulatory requirements.
- Created specific legal statements and disclosures regarding the use
of the AI on its client's advertising.
The technology company has:
- Provided guidance and resources to developers to address
environmental concerns.
- Build technical guidance on how to measure and mitigate bias in the
LLM.
- Provided tools and resources to measure bias specific to the LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Mapped and mitigated potential societal harms and large-scale
impacts.
- Followed applicable regulatory requirements and industry standards.
- Created specific legal statements and disclosures regarding the LLM,
including with respect to IP and rights to data.
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?

A. The marketing agency's client.
B. The tech company.
C. The data aggregator.
D. The marketing agency.

Answer: B

NEW QUESTION # 73
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