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GAICC ISO-IEC-42001-Foundation Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Core Components of the Artificial Intelligence Management System (AIMS)	40%	- Operational Controls and Documentation Requirements 1. AI model change records 2. Data governance procedures 3. AI incident logs and operational documentation - AI Policy and Organizational Values 1. Purpose and role of an AI policy 2. Integration of AI risk management into organizational values 3. Leadership commitment to fairness and transparency - AI Risk and Impact Assessments 1. AI impact assessment documentation 2. AI System Impact Assessment (AI-SIA) 3. AI risk assessment concepts - Performance Evaluation and Continual Improvement 1. Internal reviews 2. Continual improvement through the PDCA cycle 3. Corrective actions 4. AI system monitoring - Main Requirements of ISO/IEC 42001 Clauses 4–10 1. Improvement 2. Operation 3. Leadership 4. Context of the organization 5. Planning 6. Support 7. Performance evaluation
Topic 2: Responsible AI, Ethics, and Stakeholder Awareness	20%	- Stakeholder Roles in AI Governance 1. External stakeholders Customers Regulators Society 2. Internal stakeholders Organizational leadership AI developers Compliance teams - Ethical Principles of Responsible AI 1. Accountability 2. Fairness and non-discrimination 3. Human oversight 4. Privacy - Transparency and Explainability in AI Systems 1. Transparency in AI systems 2. Understandable AI decisions for users and regulators 3. Explainability of AI decisions - Communication, Stakeholder Engagement, and Trust 1. Responsible AI policies 2. Public disclosures and stakeholder communication 3. Building trust through stakeholder engagement 4. AI ethics statements
Topic 3: Fundamentals of AI Governance and the ISO/IEC 42001 Framework	40%	- Structure and Clauses of ISO/IEC 42001 1. Structure and sequence of ISO/IEC 42001 2. Requirements covered by Clauses 4–10 3. Plan-Do-Check-Act (PDCA) cycle - Relationship Between ISO/IEC 42001 and Other Management Standards 1. Relationship with ISO/IEC 27001 2. Relationship with ISO 9001 3. Relationship with ISO/IEC 27701 - Key Terms and Definitions Related to AI Governance 1. AI lifecycle and continual improvement 2. Artificial Intelligence Management System (AIMS) 3. Accountability, bias, and transparency - Purpose and Scope of ISO/IEC 42001 1. Purpose and objectives of the Artificial Intelligence Management System (AIMS) 2. Organizational adoption of AI governance frameworks 3. Alignment with ethical AI principles and sustainable digital transformation - Risk-Based Thinking in AI Governance 1. AI risk assessment and mitigation 2. Identification of AI-related risks 3. Integration of AI risks with organizational objectives

Section

Weight

Objectives

Topic 1: Core Components of the Artificial Intelligence Management System (AIMS)

40%

- Operational Controls and Documentation Requirements

1. AI model change records
2. Data governance procedures
3. AI incident logs and operational documentation

- AI Policy and Organizational Values

1. Purpose and role of an AI policy
2. Integration of AI risk management into organizational values
3. Leadership commitment to fairness and transparency

- AI Risk and Impact Assessments

1. AI impact assessment documentation
2. AI System Impact Assessment (AI-SIA)
3. AI risk assessment concepts

- Performance Evaluation and Continual Improvement

1. Internal reviews
2. Continual improvement through the PDCA cycle
3. Corrective actions
4. AI system monitoring

- Main Requirements of ISO/IEC 42001 Clauses 4–10

1. Improvement
2. Operation
3. Leadership
4. Context of the organization
5. Planning
6. Support
7. Performance evaluation

Topic 2: Responsible AI, Ethics, and Stakeholder Awareness

20%

- Stakeholder Roles in AI Governance

1. External stakeholders
- Customers
- Regulators
- Society
2. Internal stakeholders
- Organizational leadership
- AI developers
- Compliance teams

- Ethical Principles of Responsible AI

1. Accountability
2. Fairness and non-discrimination
3. Human oversight
4. Privacy

- Transparency and Explainability in AI Systems

1. Transparency in AI systems
2. Understandable AI decisions for users and regulators
3. Explainability of AI decisions

- Communication, Stakeholder Engagement, and Trust

1. Responsible AI policies
2. Public disclosures and stakeholder communication
3. Building trust through stakeholder engagement
4. AI ethics statements

Topic 3: Fundamentals of AI Governance and the ISO/IEC 42001 Framework

40%

- Structure and Clauses of ISO/IEC 42001

1. Structure and sequence of ISO/IEC 42001
2. Requirements covered by Clauses 4–10
3. Plan-Do-Check-Act (PDCA) cycle

- Relationship Between ISO/IEC 42001 and Other Management Standards

1. Relationship with ISO/IEC 27001
2. Relationship with ISO 9001
3. Relationship with ISO/IEC 27701

- Key Terms and Definitions Related to AI Governance

1. AI lifecycle and continual improvement
2. Artificial Intelligence Management System (AIMS)
3. Accountability, bias, and transparency

- Purpose and Scope of ISO/IEC 42001

1. Purpose and objectives of the Artificial Intelligence Management System (AIMS)
2. Organizational adoption of AI governance frameworks
3. Alignment with ethical AI principles and sustainable digital transformation

- Risk-Based Thinking in AI Governance

1. AI risk assessment and mitigation
2. Identification of AI-related risks
3. Integration of AI risks with organizational objectives

>> ISO-IEC-42001-Foundation無料問題 <<

ISO-IEC-42001-Foundation復習資料、ISO-IEC-42001-Foundation出題内容

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GAICC ISO/IEC 42001 Foundation 認定 ISO-IEC-42001-Foundation 試験問題 (Q25-Q30):

質問 # 25
A foundation candidate is asked to identify which attributes an AI policy must include to satisfy ISO/IEC
42001 Clause 5.2. The candidate is given four short summary statements about an AI policy drafted by a logistics company that uses route-optimisation and demand-forecast models. The policy is one printed page, signed by the chief executive, and posted on the staff intranet. Which summary BEST reflects the minimum attributes Clause 5.2 requires of a documented AI policy?

A. The policy promises to obtain ISO/IEC 42001 certification within twelve months, names the chosen certification body, and commits the chief executive to attend each surveillance audit in person to answer auditor questions.
B. The policy lists every AI model the organisation operates, names the data scientist accountable for each model, and specifies the exact retraining cadence in months for the algorithms used by the logistics planning team.
C. The policy is appropriate to the organisation ' s purpose, provides a framework for AI objectives, commits to satisfying applicable requirements, and commits to continual improvement of the AI management system.
D. The policy mirrors the wording of the EU AI Act in full, copies the relevant sections of the local privacy statute verbatim, and republishes the text of Annex A controls so staff have one combined reference document.

正解：C

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Clause 5.2 (AI Policy) defines four mandatory statutory criteria that top management must satisfy when formulating the documented AI policy. Specifically, the AI policy must: (a) be appropriate to the purpose and context of the organization; (b) provide a structured framework for setting AI objectives; (c) include a formal commitment to satisfy applicable legal, statutory, regulatory, and contractual requirements; and (d) include a clear commitment to the continual improvement of the AI management system. The policy is an executive- level strategic commitment rather than a granular technical asset inventory or model configuration logbook.
Prescribing operational model parameters, copying external legislation verbatim, or committing to prospective audit timelines conflates operational procedures with core leadership governance policies.
Reference: ISO/IEC 42001:2023 Clause 5.2 (AI policy); GAICC Foundation Module 5 (Policy Formulation & Strategic Intent).

質問 # 26
A mid-sized SaaS company has implemented its AIMS and now wants to obtain certification against ISO/IEC
42001. The CISO asks which standard governs the bodies that will conduct the external certification audit, so she can check the credentials of the shortlisted firms. Which standard is the right reference?

A. ISO/IEC 22989, which provides AI vocabulary and concepts and is the normative reference cited inside ISO/IEC 42001 for terminology used during audits.
B. ISO/IEC 38507, which sets governance implications of AI use for governing bodies and supports board- level oversight in organisations adopting ISO/IEC 42001.
C. ISO/IEC 17021-1, which sets requirements for bodies providing audit and certification of management systems and applies to ISO/IEC 42001 certification bodies.
D. ISO/IEC 27001, which sets requirements for information security management systems and is widely used by SaaS vendors that also pursue ISO/IEC 42001.

正解：C

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Conformity assessment bodies (CABs) that evaluate organizations for certification against management system standards must themselves satisfy the stringent competence and operational criteria set out in ISO/IEC
17021-1 (Conformity assessment - Requirements for bodies providing audit and certification of management systems). ISO/IEC 17021-1 establishes the global benchmark for impartiality, audit methodology, auditor qualifications, and quality control that certification bodies must demonstrate to national accreditation bodies (such as UKAS, ANAB, or DAkkS). While ISO/IEC 22989 is the foundational terminology standard and ISO
/IEC 38507 provides corporate governance guidance for boards, neither standard governs the operations of third-party certification bodies. Checking that a certification body holds accreditation under ISO/IEC 17021-1 ensures that the resulting certificate is credible, verifiable, and internationally recognized.
Reference: ISO/IEC 17021-1; GAICC Foundation Module 2 (The Conformity Assessment Ecosystem & Third-Party Auditing).

質問 # 27
A new compliance analyst is reading ISO/IEC 42001 for the first time and asks her mentor what the acronym ' AIMS ' stands for in the context of this standard. According to Clause 3 and GAICC Foundation Module 3, which option provides the correct expansion?

A. AI Monitoring and Surveillance-the runtime telemetry layer that watches deployed AI models for performance drift, anomalous outputs, and integrity breaches.
B. Audit Integrity Maintenance Schedule-the calendar that governs internal audit cadence, surveillance audits, and recertification audits across a three-year cycle.
C. AI Management System-the set of interrelated elements an organisation uses to establish policies and objectives for its AI activities and the processes to achieve them.
D. AI Impact Mitigation Strategy-the documented plan describing how an organisation will treat residual AI risks identified through impact assessment activities.

正解：C

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
In accordance with ISO/IEC 42001 Clause 3 (Terms and definitions) and the Harmonized Structure common to all modern ISO management system standards, the acronym " AIMS " formally stands for " Artificial Intelligence Management System. " Clause 3 defines a management system as a set of interrelated or interacting elements of an organization to establish policies and objectives, as well as processes to achieve those objectives. Therefore, the AIMS encompasses the governance framework, policies, structural controls, operational lifecycles, risk management methodologies, impact assessment mechanisms, and continuous improvement loops established to direct, govern, and control an entity's AI-related undertakings. It must not be conflated with point-in-time runtime telemetry platforms (AI monitoring), risk treatment blueprints, or audit scheduling timelines.
Reference: ISO/IEC 42001:2023 Clause 3.1; GAICC Foundation Module 3 (Vocabulary, Concepts, and Structural Frameworks).
Batch 2: Questions 11 to 20

質問 # 28
A health-tech startup deploys a chatbot that answers patient questions about symptom management. Internally the team uses a large language model behind the chatbot but the public interface does not indicate that the responses are AI-generated. The product manager argues that disclosure would reduce user trust. The AI governance lead disagrees on responsible-AI grounds. Which response BEST reflects the transparency principle in ISO/IEC 42001 Annex B and related Annex A controls?

A. Withhold the AI nature of the chatbot from users to preserve trust during the launch period, on the basis that disclosing AI involvement could discourage patients from using a tool that the team believes will benefit them medically over time.
B. Disclose to users that the chatbot is an AI system, describe its intended purpose and limitations in plain language, and make this information accessible at the point of interaction so that users can make informed choices about using it.
C. Add a footnote to the corporate website ' s privacy policy stating that the company uses AI in some of its products, without further disclosure inside the chatbot interface itself, so that transparency is technically addressed at the organisation level.
D. Publish a detailed technical specification of the model architecture and training procedure on the company ' s developer blog and treat that publication as sufficient transparency for end users who interact with the chatbot through the patient-facing interface.

正解：B

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Under ISO/IEC 42001 Annex B (Transparency and Explainability) and the control objectives outlined in Annex A (specifically domain A.8 regarding AI system operation and transparency to interested parties), deploying an AI interface that conceals its non-human nature from end-users directly contravenes responsible AI requirements. In healthcare and clinical-adjacent workflows, end-users make critical personal decisions based on system responses. Transparency requires that individuals interacting directly with an AI system are explicitly notified that they are engaging with artificial intelligence, unless obvious from context.
Furthermore, disclosure must be meaningful: it should articulate the system ' s intended purpose, its technical and diagnostic limitations, and points of contact for human escalation, delivered in plain, accessible language directly at the interface level. Concealing AI operation, burying disclaimers within obscure corporate privacy policies, or relying solely on technical engineering blog posts fails the standard ' s transparency mandate.
Reference: ISO/IEC 42001:2023 Annex B.5 (Transparency and explainability); Control Domain A.8 (AI system operations); GAICC Foundation Module 6 (Transparency, Disclosure, and User Trust).

質問 # 29
A municipal council trains an AI tool to predict pothole locations using crowdsourced reports submitted through a smartphone app. After deployment, the tool consistently recommends maintenance in wealthier suburbs where smartphone uptake is highest, while under-recommending in lower-income areas where reports are sparse. A foundation learner is asked which type of bias BEST describes what is occurring and which responsible-AI consideration is most directly affected.

A. Confirmation bias, because the council ' s road maintenance team trusts the tool more than it should, and that trust reinforces the existing recommendation pattern through downstream operational scheduling that follows the tool ' s outputs.
B. Automation bias, because human operators defer to the AI tool ' s recommendations without applying their own knowledge of which neighbourhoods would benefit from inspection regardless of whether residents reported damage through the app.
C. Label bias, because the council misclassified individual reports during the training data labelling stage, and the resulting label errors propagated into the model ' s prediction patterns across the different neighbourhoods served by the council.
D. Sample selection bias, because the training data systematically over-represents neighbourhoods with higher smartphone uptake and under-represents others, distorting the fairness of the maintenance outcomes the model recommends.

正解：D

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
The scenario exemplifies sample selection bias (a form of statistical and data collection bias), which directly compromises the foundational responsible AI principle of fairness. When training datasets are constructed from crowdsourced smartphone applications, the underlying observations inevitably reflect socioeconomic disparities in device access, digital literacy, and civic participation rather than the true objective distribution of physical road defects. Because higher-income demographics submit a disproportionately high volume of reports, the algorithmic training distribution over-represents affluent municipal zones while systematically under-representing under-resourced communities. Under ISO/IEC 42001 Annex B and controls in domains A.
6 (AI system life cycle) and A.7 (Data for AI systems), organizations must implement data quality assurance processes that evaluate dataset representativeness, data provenance, and sampling parity to preemptively identify and mitigate systemic bias before operational deployment.
Reference: ISO/IEC 42001:2023 Annex B.3 (Fairness); Control Domain A.7 (Data for AI systems); GAICC Foundation Module 6 (Algorithmic Bias, Fairness, and Data Governance).

質問 # 30
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