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IAPP AIGP Exam Syllabus Topics:

Section	Weight	Objectives
Governance of AI Development	21–25%	- Model management, version control and quality assurance - Requirements gathering, risk assessment and mitigation - Testing, validation, documentation and transparency - Governance through design and lifecycle management
Laws, Regulations, Standards and Frameworks	24–28%	- International standards (ISO 42001, NIST AI RMF, etc.) - Global and regional regulations (EU AI Act, US, etc.) - Sector-specific requirements and compliance obligations - Alignment with data protection and privacy laws
Foundational Concepts of AI Governance	24–28%	- Principles of responsible, ethical and trustworthy AI - Governance frameworks and core concepts - Impacts, risks and benefits of AI systems - Fundamental AI definitions, types and characteristics
Governance of AI Deployment and Use	21–25%	- Lifecycle maintenance, decommissioning and updates - Selection, procurement and third-party management - Incident response, accountability and audit - Implementation, monitoring and ongoing oversight

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IAPP Certified Artificial Intelligence Governance Professional 認定 AIGP 試験問題 (Q22-Q27):

質問 # 22
CASE STUDY
Please use the following to answer the next question:
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address AI governance.
The marketing company has:
- Entered into a contract with the technology company with suitable
representations and warranties.
- Completed an impact assessment on the LLM for this intended use.
- Built technical guidance on how to measure and mitigate bias in the
LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Followed applicable regulatory requirements.
- Created specific legal statements and disclosures regarding the use
of the AI on its client's advertising.
The technology company has:
- Provided guidance and resources to developers to address
environmental concerns.
- Build technical guidance on how to measure and mitigate bias in the
LLM.
- Provided tools and resources to measure bias specific to the LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Mapped and mitigated potential societal harms and large-scale
impacts.
- Followed applicable regulatory requirements and industry standards.
- Created specific legal statements and disclosures regarding the LLM,
including with respect to IP and rights to data.
All of the following results would be considered biased outputs from this AI system EXCEPT:

A. The images of female workers are hyper-sexualized.
B. The generated ads are sent to construction companies, not individual workers.
C. The advertising text generated for female audiences focuses on color and style.
D. The content generated for minority construction workers is insufficient.

正解：B

解説：
Sending generated ads to construction companies rather than individual workers is a targeting choice, not an example of biased output from the AI system itself. The other options reflect biased or stereotypical content produced by the AI.

質問 # 23
Retrieval-Augmented Generation (RAG) is defined as:

A. Fine tuning LLMs to minimize biased outputs.
B. Applying advanced filtering techniques to the LLMs.
C. Reducing computational processing requirements of the LLMs.
D. Combining LLMs with private knowledge bases to improve their outputs.

正解：D

解説：
RAG integrates large language models with external or private knowledge bases so the model can retrieve relevant information and generate more accurate, context-aware outputs.

質問 # 24
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. Interpretable AI
C. Trustworthy AI
D. Explainable AI

正解：D

解説：
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.

質問 # 25
Which risk management framework/guide/standard focuses on value-based engineering methodology?

A. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
B. ISO 31000 Guidelines (Risk Management).
C. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for AI Systems.
D. ISO/IEC Guide 51 (Safety).

正解：A

解説：
IEEE 7000-2021 focuses on value-based engineering by integrating ethical concerns into system design processes.

質問 # 26
A company deploys an AI model for fraud detection in online transactions. During its operation, the model begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent.
Which is the best step the company should take to address this development?

A. Conduct training for customer service teams to handle flagged transactions.
B. Maintain records of all false positives.
C. Dedicate more resources to monitor the model.
D. Deactivate the model until an assessment is made.

正解：D

解説：
When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.
From theAI Governance in Practice Report2025:
"Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems." (p. 12, 35)

質問 # 27
......

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