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IAPP AIGP Exam Overview:

Certification Vendor:IAPP
Exam Name:IAPP Certified Artificial Intelligence Governance Professional
Exam Number:AIGP
Certificate Validity Period:2 Years
Exam Price:USD 550
Passing Score:300
Exam Format:Multiple-choice
Related Certifications:CIPP/A
CIPT
CIPP/E
CIPP/US
CIPM
Real Exam Qty:100
Available Languages:English
Exam Duration:150 minutes
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 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 (Q172-Q177):

NEW QUESTION # 172
Which of the following most encourages accountability over AI systems?

Answer: A

Explanation:
Defining clear roles and responsibilities for AI stakeholders is essential to establish accountability throughout the AI system's lifecycle.


NEW QUESTION # 173
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running AI applications?

Answer: C

Explanation:
GPUs excel at parallel processing, enabling them to run many tasks concurrently, which speeds up AI computations significantly compared to CPUs.


NEW QUESTION # 174
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network ' s existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, what is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?

Answer: D

Explanation:
In the design phase of integrating data from different sources, identifying fits and gaps is crucial. This process involves understanding how well the data from the clinical research partner aligns with the healthcare network
' s existing data. It ensures that the combined data set is coherent and can be effectively used for training the AI algorithm. This step helps in spotting any discrepancies, inconsistencies, or missing data that might affect the performance and accuracy of the AI model. It directly addresses the integrity and compatibility of the data, which is foundational before applying any privacy-enhancing technologies, labeling, or evaluating the origin of the data. Reference: AIGP Body of Knowledge on Data Integration and Quality.


NEW QUESTION # 175
All of the following are obligations of providers under the EU AI Act EXCEPT?

Answer: C

Explanation:
D is the exception because the EU AI Act distinguishes obligations imposed on providers from those imposed on deployers. Under Article 16, providers of high-risk AI systems must maintain required documentation and automatically generated logs under their control. Article 17 requires providers to establish a documented quality management system, which includes setting up, implementing, and maintaining a post-market monitoring system. These provisions support A, B, and C. By contrast, Article 26 requires deployers to take appropriate organizational and technical measures to use high-risk systems according to the provider ' s instructions and to assign human oversight to suitably competent, trained, and authorized natural persons.
Providers must design high-risk systems so effective human oversight is possible, but actually ensuring oversight during operational use in accordance with instructions is principally a deployer obligation.


NEW QUESTION # 176
Scenario:
A European AI technology company was found to be non-compliant with certain provisions of the EU AI Act.
The regulator is considering penalties under the enforcement provisions of the regulation.
According to the EU AI Act, which of the following non-compliance examples could lead to fines of up to
€15 million or 3% of annual worldwide turnover(whichever is higher)?

Answer: C

Explanation:
The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation. A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €15 million or 3% of annual global turnover.
From the AIGP ILT Guide - EU AI Act Module:
"Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover." AI Governance in Practice Report2025supports this:
"Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article
71(3) of the EU AI Act."
Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.


NEW QUESTION # 177
......

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