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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP (International Association of Privacy Professionals)
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional Exam
Exam Number:	AIGP
Passing Score:	300 (scaled score out of 500)
Exam Duration:	165 (including 15-minute optional break)
Exam Format:	Scenario-based, Multiple-choice
Related Certifications:	CIPM CIPT CIPP
Exam Price:	USD 649 (members) / USD 799 (non-members)
Available Languages:	English
Certificate Validity Period:	2 years
Real Exam Qty:	100
Recommended Training:	Official AIGP Body of Knowledge & Study Guide IAPP Training & Resources
Exam Registration:	IAPP Official Registration Pearson VUE Scheduling
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online remote proctored or in-person at Pearson VUE test centers
Pre Condition:	No formal prerequisites; open to all professionals
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 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 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of the following compliance related controls within an organization is most easily adapted to identify AI risks?

A. Privacy training.
B. Penetration testing.
C. Transfer risk assessments.
D. Privacy impact assessments.

Answer: D

Explanation:
Privacy impact assessments are already structured to identify risks related to data use, impacts on individuals, and mitigation strategies, making them the easiest existing control to adapt for evaluating AI-related risks.

NEW QUESTION # 15
CASE STUDY
Please use the following to answer the next question:
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
The data processed by the AI system would be classified as:

A. Non-personal data, as long as it is not linked to a user ID.
B. Organizational data, since it is part of the authentication process.
C. Special category data, if it can be used to uniquely identify a person.
D. Non-sensitive personal data, since it does not reveal information about health, gender or race.

Answer: C

Explanation:
Behavioral biometric data used to uniquely identify individuals is considered special category data due to its sensitivity and potential impact on privacy.

NEW QUESTION # 16
A deployer discovers that a high-risk AI recruiting system has been making widespread errors, resulting in harms to the rights of a considerable number of EU residents who are denied consideration for jobs for improper reasons such as ethnicity, gender and age.
According to the EU AI Act, what should the company do first?

A. Identify any decisions that may have been improperly made and re-open them for human review.
B. Submit an incomplete report to the relevant market authority immediately and follow up with a complete report as soon as possible.
C. Notify the provider, the distributor, and finally the relevant market authority of the serious incident.
D. Conduct a thorough investigation of the serious incident within the 15 day timeline and present the completed report to the relevant market authority.

Answer: C

Explanation:
Under theEU AI Act, serious incidents involvinghigh-risk AI systemsmust be reported. The deployer is required topromptly inform the provider and relevant authoritiesabout the issue.
From theAI Governance in Practice Report 2025:
"Serious incidents involving high-risk systems... must be reported to the provider and relevant market surveillance authority." (p. 35)
"Timely reporting is required when AI systems result in or may result in violations of fundamental rights." (p. 35)

NEW QUESTION # 17
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT:

A. Gradient descent, which initially sets weights to arbitrary values, and then at each step changes them.
B. Momentum, which improves the convergence speed and stability of neural network training.
C. Backpropagation, which starts from the last layer working backwards.
D. Autoregression, which analyzes and makes predictions about time-series data.

Answer: D

Explanation:
Autoregression is a statistical modeling technique for time-series analysis, not a weight optimization technique used in deep learning neural networks.

NEW QUESTION # 18
All of the following are included within the scope of post-deployment AI maintenance EXCEPT:

A. Evaluating the need for an audit under certain standards.
B. Ensuring that all model components are subject to a control framework.
C. Defining thresholds to conduct new impact assessments.
D. Dedicating experts to continually monitor the model output.

Answer: B

Explanation:
Ensuring all model components are subject to a control framework is typically addressed during development and governance setup, not specifically post-deployment maintenance.

NEW QUESTION # 19
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