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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 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 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 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 (Q163-Q168):

NEW QUESTION # 163
The processes and methods that allow human users to understand and trust the outputs produced by AI are important in addressing which key regulatory concern?

A. Responsible AI
B. Trustworthy AI
C. Interpretable AI
D. Explainable AI

Answer: D

Explanation:
The correct answeris ExplainableAi becauseit specifically refers tothe abilityof a systemto describethe logic behind its decisions oroutput ina way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressingthe black-boxproblem inAI.
From the AIGP ILT Participant Guide (Module on Transparency and Explainability):
"Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems." Also, according to the AI Governance in Practice Report2025:
"Explainability refers to the representation of the underlying mechanisms of the AI system's operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained." Thus, while Trustworthy and Responsible AI are broader concepts,explainabilityspecifically targetsthe regulatoryconcern about understanding outputs.

NEW QUESTION # 164
Scenario:
A U.S.-based AI governance professional is evaluating resources from the National Institute of Standards and Technology (NIST) to guide the organization's AI risk assessment strategy. They are particularly interested in programs focused on assessing AI-specific impacts.
The main purpose of NIST's Assessing Risks and Impacts of AI (ARIA) program is to:

A. Promote interoperability across AI systems
B. Offer a regulatory sandbox for risk reporting
C. Provide a suite of resources to manage risks
D. Pilot new standards for AI red-teaming

Answer: C

Explanation:
The correct answer is A. The ARIA program by NIST is explicitly designed to support stakeholders in understanding and managing the risks and impacts of AI systems.
From the AIGP ILT Guide - U.S. Risk Frameworks Module:
"NIST's ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use." Also cited in the AI Governance in Practice Report 2024 (Frameworks Section):
"ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications." ARIA is not a red-teaming or sandbox program-it's an assessment and governance resource.

NEW QUESTION # 165
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?

A. GPUs can run every task on a computer, making them more robust than CPUs.
B. GPUs can access memory quickly, resulting in lower latency than CPUs.
C. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
D. GPUs run many tasks concurrently, resulting in faster processing.

Answer: D

Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference:
AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.

NEW QUESTION # 166
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT:

A. Retaining system-generated logs for at least six months.
B. Conducting a conformity assessment.
C. Publishing a detailed report on the training data used.
D. Conducting post-market monitoring.

Answer: C

NEW QUESTION # 167
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. Notify the provider, the distributor, and finally the relevant market authority of the serious incident.
B. Submit an incomplete report to the relevant market authority immediately and follow up with a complete report as soon as possible.
C. Conduct a thorough investigation of the serious incident within the 15 day timeline and present the completed report to the relevant market authority.
D. Identify any decisions that may have been improperly made and re-open them for human review.

Answer: A

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 Report2025:
"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 # 168
......

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