ISO-IEC-42001-Foundation考試 & ISO-IEC-42001-Foundation考試重點

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

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

Section

Weight

Objectives

Fundamentals of AI Governance and the ISO/IEC 42001 Framework

40%

- Key Terms and Definitions Related to AI Governance

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

- Structure and Clauses of ISO/IEC 42001

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

- Purpose and Scope of ISO/IEC 42001

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

- Relationship Between ISO/IEC 42001 and Other Management Standards

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

- Risk-Based Thinking in AI Governance

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

Core Components of the Artificial Intelligence Management System (AIMS)

40%

- AI Policy and Organizational Values

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

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

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

- AI Risk and Impact Assessments

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

- Operational Controls and Documentation Requirements

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

Responsible AI, Ethics, and Stakeholder Awareness

20%

- Transparency and Explainability in AI Systems

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

- Ethical Principles of Responsible AI

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

- Communication, Stakeholder Engagement, and Trust

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

- Stakeholder Roles in AI Governance

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

>> ISO-IEC-42001-Foundation考試 <<

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最新的 GAICC Certification ISO-IEC-42001-Foundation 免費考試真題 (Q23-Q28):

問題 #23
A workshop exercise asks foundation candidates to evaluate four candidate sentences for inclusion in a one- page AI policy at an e-commerce retailer. The retailer uses AI for product recommendations, fraud detection, and customer-service triage. The intent is to capture a single sentence that aligns with the four attributes Clause 5.2 requires. Which sentence is the BEST single-line expression of those Clause 5.2 attributes?

A. The retailer will manage its AI systems in a manner appropriate to its purpose, satisfy applicable requirements, set and pursue measurable AI objectives, and continually improve its AI management system in line with this policy.
B. The retailer will appoint an AI ethics committee composed of representatives from product, engineering, legal, and marketing with quarterly meetings, decisions recorded, and an annual report submitted to the board ' s risk subcommittee.
C. The retailer commits to certifying every AI system it deploys against ISO/IEC 42001 within a twelve- month window, with surveillance audits scheduled annually thereafter under the chosen third-party certification body for each system in operation.
D. The retailer ' s recommendation engine will be retrained every fortnight using the most recent behavioural data from logged-in customers, and the fraud-detection model will be retuned monthly to reflect emerging patterns of attempted account compromise.

答案：A

解題說明：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Clause 5.2 (AI policy) sets out four non-negotiable structural requirements that any documented AI policy must encompass. The policy must: (1) be appropriate to the purpose and context of the organization; (2) provide a framework for setting and reviewing AI objectives; (3) include a commitment to satisfy applicable legal, regulatory, and stakeholder requirements; and (4) include an explicit commitment to the continual improvement of the AI management system. Option B explicitly encapsulates each of these four core elements within a concise governance statement. In contrast, specifying technical model retraining cadences (Option A) belongs in operational SOPs; defining committee membership rosters (Option C) addresses operational execution rather than policy direction; and declaring specific certification schedules (Option D) addresses external conformity timelines rather than organizational policy obligations.
Reference: ISO/IEC 42001:2023 Clause 5.2 (AI policy); GAICC Foundation Module 5 (Leadership, Policy Architecture, and Governance Commitments).

問題 #24
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. 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.
B. 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.
C. 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.
D. 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.

答案：C

解題說明：
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).

問題 #25
A foundation candidate is asked to draft the agenda for a hospital network ' s first AIMS management review, scheduled six months after policy adoption. The chief executive will chair, and the appointed AIMS reporting officer will present. The case workshop offers four candidate agendas. Which agenda BEST reflects the leadership and review expectations of ISO/IEC 42001 Clauses 5 and 9.3 at this stage of the management system ' s life cycle?

A. An agenda centred on vendor-management updates from the procurement team, with limited coverage of internal AIMS performance, risk-assessment outputs, impact-assessment status, or the suitability of the AI policy at six months in.
B. An agenda dominated by detailed technical model-architecture walkthroughs delivered by the data science team, with little time devoted to governance, risk treatment, or the decisions top management is expected to make at the close of the review.
C. An agenda that reviews AIMS performance, status of AI risk and impact assessments, audit and monitoring outputs, the continuing suitability of the AI policy, and resource and improvement decisions for top-management action.
D. A single-item agenda focused on reaffirming the AI policy signature, with no review of AIMS performance indicators, risk and impact-assessment status, or resource adequacy at this early stage of the management system ' s operating life cycle.

答案：C

解題說明：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Clause 9.3 (Management review) requires top management to review the organization ' s AI management system at planned intervals to ensure its continuing suitability, adequacy, and effectiveness. The standard sets out mandatory review inputs, which include: status of actions from previous reviews; changes in internal and external context; fulfillment of AI objectives; information on AIMS performance (including nonconformities, monitoring and measurement results, and audit findings); outputs from AI risk assessments and AI System Impact Assessments (Clause 6.1.4); and resource adequacy. Furthermore, the review must culminate in documented outputs regarding improvement opportunities and resource allocation decisions. An agenda skewed exclusively toward technical engineering minutiae, narrow supplier updates, or superficial policy reaffirmation fails to satisfy the comprehensive governance oversight mandated under Clause 9.3 and Clause
5.1.
Reference: ISO/IEC 42001:2023 Clause 9.3 (Management review); Clause 5.1; GAICC Foundation Module 9 (Performance Evaluation & Management Review).

問題 #26
A compliance manager is preparing a slide for the leadership team to explain the difference between accreditation and certification in the ISO/IEC 42001 ecosystem. She wants a one-sentence working definition of an accreditation body that aligns with GAICC Foundation Module 2. Which option is the BEST fit?

A. An accreditation body is the internal audit function within an organisation that reviews the AIMS against ISO/IEC 42001 before any external certification audit is conducted.
B. An accreditation body is the consulting firm engaged by an organisation to help draft its AI policy, conduct gap analyses, and prepare evidence packs for a Stage 1 audit.
C. An accreditation body is an authoritative third party that assesses and formally recognises the competence of certification bodies to audit against standards such as ISO/IEC 42001.
D. An accreditation body is the publisher of an ISO standard, responsible for drafting the requirements text, maintaining revisions, and updating the harmonised structure terms.

答案：C

解題說明：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
In the global conformity assessment hierarchy governed by the International Accreditation Forum (IAF) and ISO/IEC 17011, there is a distinct structural separation between certification and accreditation. Organizations seeking to validate their AIMS undergo a certification audit performed by a Certification Body (Conformity Assessment Body). In turn, the competence, impartiality, and procedural integrity of the Certification Body are evaluated, formally approved, and surveilled by an authoritative Accreditation Body (such as UKAS in the UK, ANAB in the United States, or DAkkS in Germany). Accreditation acts as the authoritative mechanism that validates certification bodies, ensuring mutual recognition of ISO/IEC 42001 certificates across global jurisdictions. Accreditation bodies do not provide consulting services, conduct internal enterprise audits, or author the underlying international standards (which is the purview of ISO/IEC JTC 1/SC 42).
Reference: ISO/IEC 17011; ISO/IEC 17021-1; GAICC Foundation Module 2 (The Global Conformity Assessment Infrastructure).

問題 #27
A start-up founder argues that ISO/IEC 42001 is irrelevant to her company because it neither develops AI models nor sells AI products-it merely uses a third-party generative AI assistant to draft customer support replies. The compliance lead disagrees and points to the standard ' s scope as described in the GAICC Foundation materials. Which statement BEST captures who ISO/IEC 42001 is intended for?

A. Organisations that build and train their own AI models from scratch, since the standard targets AI developers rather than downstream users of third-party vendor tools.
B. Enterprises above a defined revenue threshold, since the cost and complexity of an AI management system is disproportionate for smaller firms or early-stage start-ups.
C. Any organisation that develops, provides, or uses AI systems, irrespective of size, sector, or AI maturity, including those that consume AI through third-party tools.
D. Public-sector bodies subject to AI legislation, since voluntary management system standards are intended for regulated entities rather than commercial start-ups.

答案：C

解題說明：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Clause 1 (Scope) of ISO/IEC 42001 specifies that the standard is universally applicable to any organization, regardless of its size, nature, sector, or operational maturity, that develops, provides, or uses products or services that utilize artificial intelligence systems. The standard explicitly rejects the premise that AI governance is exclusively relevant to foundational frontier model builders or commercial software vendors.
Downstream deployers and end-users of third-party commercial tools-such as enterprises leveraging third- party large language models for customer engagement-introduce operational, legal, privacy, and reputational risks into their operational environments. Consequently, organizations acting purely as AI users must establish governance oversight over tool selection, acceptable use policies, prompt data confidentiality, operational monitoring, and vendor relationship controls governed under Annex A (specifically domain A.9).
The standard's requirements are scalable and proportionate to organizational context and risk exposure rather than headcount or development footprint.
Reference: ISO/IEC 42001:2023 Clause 1 (Scope); GAICC Foundation Module 1 (The Purpose, Applicability, and Reach of ISO/IEC 42001).

問題 #28
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