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IAPP AIGP 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 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.
トピック 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.
トピック 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 認定 AIGP 試験問題 (Q194-Q199):

質問 # 194
Scenario:
Business A provides grammar and writing assistance tools and licenses a generative AI model from Business B to enhance its offerings. Business A is concerned that the AI model might produce inappropriate or toxic content and wants to implement governance processes to prevent this.
Which of the following governance processes should Business A take tobest protect its usersagainst potentially inappropriate text?

A. Business A should ask Business B for detailed documentation on the generative AI model's training data and whether it contained toxic or obscene sources
B. Business A should test that the AI model performs as expected and meets their minimum requirements for filtering toxic or obscene text
C. Business A should fine-tune the AI model on user-generated text that has been verified to be appropriate
D. Business A should establish a user reporting feature that allows users to flag toxic or obscene text, and report any incidents to Business B

正解：B

解説：
The correct answer isB. According to responsible AI practices,pre-deployment testingto ensurethe model behaves as expected and aligns with organizational requirements is critical.
From the AIGP ILT Guide:
"Testing for unacceptable outcomes such as toxicity, discrimination, or hallucinations should be included in the AI governance life cycle, particularly during development and prior to deployment." Also emphasized in the AI Governance in Practice Report2025:
"Organizations must verify legal and regulatory compliance, monitor performance, and mitigate risksprior to deployment." Testing the model tomeet safety and appropriateness standardsis more proactive and preventive than relying solely on user reporting or requesting documentation.

質問 # 195
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

A. Office of Science and Technology Policy (OSTP).
B. National Institute of Standards and Technology (NIST).
C. Department of Homeland Security (DHS).
D. National Science and Technology Council (NSTC).

正解：B

解説：
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.

質問 # 196
Training data is best defined as a subset of data that is used to?

A. Enable a model to detect and learn patterns.
B. Detect the initial sources of biases to mitigate prior to deployment.
C. Resemble the structure and statistical properties of production data.
D. Fine-tune a model to improve accuracy and prevent overfitting.

正解：A

解説：
Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.

質問 # 197
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an AI system?

A. Industry practice.
B. Risk to users.
C. Cost of alternative mechanisms.
D. Feasibility.

正解：A

解説：
Industry practice is less directly relevant compared to feasibility, user risk, and cost considerations when deciding if users should have the right to opt out of AI systems.

質問 # 198
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?

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

正解：B、C、E

解説：
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.

質問 # 199
......

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