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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 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 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 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 (Q116-Q121):

NEW QUESTION # 116
An app developer is creating a to-do app that compiles users' to-do lists, conducts basic online research about the tasks, prioritizes tasks, and allows users to add descriptions and notes. However, during the app's download process, it requests the user's name, address, phone number, political and union affiliations.
Which privacy principle relevant to the responsible development of AI systems is most likely violated by this data collection practice?

A. Data portability and subject access rights.
B. Transparency and explainability.
C. Data minimization and privacy by design.
D. Purpose limitation and explicit consent.

Answer: C

Explanation:
Data minimization requires organizations to collect and process only personal information that is adequate, relevant, and necessary for the specified purpose. A basic to-do application may reasonably require limited account or contact information, but political opinions and trade-union affiliations have no apparent necessity for compiling, researching, or prioritizing tasks. Those attributes are also treated as particularly sensitive under European data-protection law. Privacy by design requires privacy safeguards and data-protection principles to be incorporated into the product from the design stage and reflected in default settings. Therefore, unnecessary collection at installation directly implicates both minimization and privacy by design. Purpose limitation may also become relevant, but explicit consent does not by itself cure unjustified overcollection. Transparency, portability, and access rights address different privacy obligations and do not best characterize the defect described here.

NEW QUESTION # 117
The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?

A. Data privacy.
B. Human alternatives, consideration and fallback.
C. High-risk mitigation standards.
D. Safe and effective systems.

Answer: C

Explanation:
The White House Blueprint for an AI Bill of Rights focuses on protecting civil rights, privacy, and ensuring AI systems are safe and effective. It includes principles like data privacy (D), human alternatives (A), and safe and effective systems (C). However, it does not specifically address high-risk mitigation standards as a distinct category (B).

NEW QUESTION # 118
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. Diversity of data sources.
C. Explainability of results.
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 Report 2025:
"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 # 119
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.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

A. To apply their own judgment to the initial assessment.
B. To solicit on-going feedback on model performance.
C. To ensure they provide transparency to applicants on the model.
D. To provide a reminder of a right to appeal.

Answer: A

Explanation:
Training underwriters ensures they understand the AI's limitations and can apply their own judgment to initial assessments, preventing over-reliance on potentially biased model outputs.

NEW QUESTION # 120
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

A. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.
B. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
C. Rules on General Purpose Al will apply after 6 months as a specific provision.
D. Fines for SMEs and startups will be proportionally capped.

Answer: A

Explanation:
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

NEW QUESTION # 121
......

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