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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.
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.
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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q170-Q175):

NEW QUESTION # 170
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:

Answer: A

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


NEW QUESTION # 171
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.
Which stakeholder group is most important in selecting the specific type of algorithm?

Answer: A


NEW QUESTION # 172
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
During the first month when the company monitors the model for bias, it is most important to:

Answer: B

Explanation:
During initial production monitoring, the most important action is to continue disparity testing to detect and quantify whether the model is producing biased outcomes across protected groups.
This directly aligns with the goal of evaluating fairness, accuracy, and reliability, enabling the organization to identify and respond to discriminatory patterns early.


NEW QUESTION # 173
Which model is best for efficiency and agility, and tailored for lower-resource settings?

Answer: B

Explanation:
Small language models (SLMs)arelightweight, requireless compute, and arebetter suited to low-resource or edge environments, making them ideal for agility and efficiency.
From general AI best practices:
"SLMs can be deployed in environments with limited computing power, ensuring lower cost and faster integration in constrained contexts." (aligned with industry-wide AI deployment strategies)


NEW QUESTION # 174
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
What are the roles and responsibilities of deployers of a proprietary model?

Answer: B,D,E

Explanation:
Deployers of proprietary models arenot responsible for design, but they are accountable for how the system performsin their context of use, including ensuring ethical behavior, performance, and legal compliance.
From theAI Governance in Practice Report2025:
"Deployers of AI systems must take reasonable steps to ensure that systems are used ethically, perform safely, and align with applicable laws and standards." (p. 11-12)
"Operational governance... includes performance monitoring protocols, incident management plans, and regulatory oversight." (p. 12) Thus:
* #A. Ethical testing- Required to mitigate misuse and unintended harms.
* #B. Ethical design- Belongs todevelopers/providers, not deployers.
* #C. Technical performance- Deployers must ensure that AI performs as expected.
* #D. System documentation- This is theprovider'sobligation.
* #E. Regulatory compliance- Deployers must ensure system use complies with applicable laws.


NEW QUESTION # 175
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