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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 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.
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 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 4	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.

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IAPP Certified Artificial Intelligence Governance Professional 認定 AIGP 試験問題 (Q52-Q57):

質問 # 52
Machine learning is best described as a type of algorithm by which?

A. Systems can mimic human intelligence with the goal of replacing humans.
B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
C. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
D. Systems can automatically improve from experience through predictive patterns.

正解：D

解説：
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

質問 # 53
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

A. To ensure interpretability of the model's predictions.
B. Account for new interpretations of the same data.
C. Adjust the model's hyper parameters to a specific use case.
D. To minimize degradation in prediction accuracy due tochanges in data.

正解：A

解説：
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.

質問 # 54
Scenario:
A global organization wants to align with international frameworks on AI governance. They are reviewing guidance from the OECD on how to incorporate broader governance tools into their AI program.
Codes of conduct and collective agreements are what type of assessment tools as defined by the Organization for Economic Cooperation and Development (OECD)?

A. Procedural
B. Analytic
C. Educational
D. Technical

正解：A

解説：
The correct answer is B - Procedural. The OECD Framework for Classifying AI Systems categorizes codes of conduct and collective agreements as procedural tools because they guide internal governance and decision-making processes.
From the AIGP ILT Participant Guide - Global Governance Models:
"Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement." AI Governance in Practice Report 2024 elaborates:
"These procedural tools support internal accountability mechanisms and ethics compliance frameworks...
they are part of soft governance."
These tools do not measure or analyze technical performance, hence they are not technical or analytic.

質問 # 55
What is the primary purpose of conducting ethical red-teaming on an AI system?

A. To improve the model's accuracy.
B. To ensure compliance with applicable law.
C. To simulate model risk scenarios.
D. To identify security vulnerabilities.

正解：C

解説：
Ethical red-teaming involves simulating adversarial scenarios to uncover risks and vulnerabilities in AI systems before deployment.

質問 # 56
All of the following are commonly adopted processes and policies in reducing potential risks introduced by third-party AI tools or applications EXCEPT:

A. Requiring new use cases of the generative AI tools or applications to be reviewed and approved by the generative AI governance body.
B. Allowing publicly available information and personally identifiable information (pii) to be incorporated into the prompt.
C. Including clauses in the procurement agreement for buyers of generative AI tools to put certain liabilities on the tool supplier.
D. Requiring an independent third-party bias audit for third-party generative AI tools.

正解：B

解説：
Allowing publicly available information and personally identifiable information to be incorporated into prompts increases risk rather than reducing it, making it the least aligned with common risk- mitigation practices for third-party AI tools.

質問 # 57
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