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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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q69-Q74):

NEW QUESTION # 69
What are the roles and responsibilities of deployers of a proprietary model? (Choose three.)

A. Regulatory compliance.
B. Technical performance.
C. System documentation.
D. Ethical testing.
E. Ethical design.

Answer: A,B,D

Explanation:
Deployers of a proprietary model are responsible for ethical testing, ensuring technical performance, and maintaining regulatory compliance when using the model in their environment and for their use case.

NEW QUESTION # 70
What is the most important reason to document the results of AI testing?

A. To create a verifiable audit trail.
B. To identify areas for red-teaming focus.
C. To support post-deployment maintenance.
D. To limit the need for future testing cycles.

Answer: A

Explanation:
Documenting AI testing results creates a verifiable audit trail, which is essential for accountability, regulatory compliance, and demonstrating that risks were properly assessed and mitigated.

NEW QUESTION # 71
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 product recommendations.
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 data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.
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 visitor's data is synthetic data that the company does not have to provide to the data subject.

Answer: B

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 # 72
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?

A. Organizational accountability.
B. Explainability of results.
C. Diversity of data sources.
D. Internal governance needs.

Answer: D

Explanation:
An AI system'sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.
From theAI Governance in Practice Report2025:
"There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization's role, footprint, broader risk-governance profile and maturity." (p. 4)
"AI governance starts with defining the corporate strategy for AI... and formulating policy standards and operational procedures to reflect industry, use case, and location." (p. 11)
* A- Organizational accountability is broader and not directly scoped by industry or function.
* C- Diversity of data sources is tied to data strategy.
* D- Explainability is more influenced by model type, not use context.

NEW QUESTION # 73
A Canadian company is developing an AI solution to evaluate candidates in the course of job interviews. Before offering the AI solution in the EU market, the company must take all of the following steps EXCEPT:

A. Engage a third-party auditor to perform a bias audit.
B. Establish a risk and quality management system.
C. Draw up technical documentation and instructions for use.
D. Register the AI solution in a public EU database.

Answer: A

Explanation:
While bias audits are important, the EU AI Act does not mandate engaging a third-party auditor; registration, risk management systems, and technical documentation are required.

NEW QUESTION # 74
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