ISO-IEC-42001-Foundation関連資格知識、ISO-IEC-42001-Foundation過去問題

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

Section	Weight	Objectives
Topic 1: Introduction to Artificial Intelligence and ISO/IEC 42001	25%	- Structure and high-level concepts of ISO/IEC 42001 - Overview of Artificial Intelligence concepts and terminology - Purpose and benefits of an Artificial Intelligence Management System (AIMS)
Topic 2: AI Management System Controls and Guidance	30%	- Data governance and lifecycle management for AI systems - Responsible AI use, transparency, and accountability - Overview of Annex A controls
Topic 3: ISO/IEC 42001 Requirements and Clauses	45%	- Improvement, nonconformity, and corrective action - Context of the organization and understanding stakeholder needs - Performance evaluation, monitoring, internal audit, and management review - Planning, AI risk assessment, and risk treatment - Leadership, commitment, and AI policy - Support, resources, competence, awareness, and communication - Operation, operational planning, and control

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ISO-IEC-42001-Foundation過去問題、ISO-IEC-42001-Foundation模擬試験サンプル

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GAICC ISO/IEC 42001 Foundation 認定 ISO-IEC-42001-Foundation 試験問題 (Q30-Q35):

質問 # 30
A foundation case workshop closes with a scenario in which a charity is about to launch an AI tool that helps caseworkers prioritise vulnerable-client outreach. The AIMS lead is asked which set of considerations the launch checklist should reflect, drawing on the responsible-AI principles in ISO/IEC 42001 Annex B together with the relevant management-system clauses. Which response BEST integrates the standard ' s expectations for a deployment of this kind?

A. Verify the model ' s predictive accuracy meets the agreed target, document the result in a one-page summary signed by the head of technology, and proceed to launch without further reference to other responsible-AI considerations for this charitable use case.
B. Postpone any structured impact assessment until after the first quarter of operational data has been gathered, on the basis that the charity will be better placed to evaluate impact once real-world caseworker usage patterns and outcome distributions have been observed.
C. Confirm that the vendor has provided a model card, treat that document as a complete responsible-AI record for the deployment, and rely on the vendor ' s representations rather than performing an internal review of the model ' s fitness for the charity ' s specific population of clients.
D. Complete an AI System Impact Assessment under Clause 6.1.4 covering fairness, transparency, human oversight, privacy, and safety considerations, define acceptance criteria, assign role responsibilities, and gain documented approval before launch.

正解：D

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Deploying an algorithm that affects vulnerable individuals requires an end-to-end integration of risk treatment, ethical safeguards, and management system governance. Clause 6.1.4 (AI system impact assessment) mandates that organizations establish, implement, and maintain processes for assessing the potential impacts of the AI system on individuals, groups, and society. In a high-consequence charitable triage scenario, impact assessment cannot be deferred until post-deployment or reduced solely to verifying statistical accuracy. The assessment must evaluate core responsible AI principles outlined in Annex B, including fairness across marginalized subgroups, human oversight to ensure caseworkers retain override discretion, transparency regarding prioritization criteria, data privacy, and unintended harms. Deploying entities must define objective acceptance thresholds, formally assign operational monitoring responsibilities under Clause
5.3, and obtain sign-off before operational launch.
Reference: ISO/IEC 42001:2023 Clause 6.1.4 (AI system impact assessment); Annex B (Guidance on responsible AI principles); GAICC Foundation Module 7 (AI Impact Assessment & Lifecycle Governance).

質問 # 31
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 consulting firm engaged by an organisation to help draft its AI policy, conduct gap analyses, and prepare evidence packs for a Stage 1 audit.
B. An accreditation body is the publisher of an ISO standard, responsible for drafting the requirements text, maintaining revisions, and updating the harmonised structure terms.
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 internal audit function within an organisation that reviews the AIMS against ISO/IEC 42001 before any external certification audit is conducted.

正解：C

質問 # 32
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. 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.
B. 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.
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. 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.

正解：C

解説：
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).

質問 # 33
A travel platform plans to train a recommendation model on five years of customer booking history, including names, contact details, and trip itineraries. The data-protection officer raises concerns about training-data use.
The AI governance lead is asked how ISO/IEC 42001 treats the relationship between AI management and personal-data protection. Which response BEST reflects the standard ' s intent on privacy as a responsible-AI consideration?

A. Personal-data protection is a relevant input to the AI System Impact Assessment under Clause 6.1.4, and the AIMS should coordinate with applicable privacy obligations rather than treating data protection as a separate concern outside the scope of AI governance.
B. The AI governance lead should remove privacy considerations from the AI System Impact Assessment scope and refer them to the privacy officer for a parallel assessment, with no formal linkage between the two assessment processes maintained within the AIMS records.
C. The AIMS should record an exception noting that training data is exempt from privacy review on grounds of model improvement, since pursuit of model accuracy provides a legitimate operational basis for the platform ' s use of historical booking information in training.
D. Personal-data protection sits entirely outside the AIMS because ISO/IEC 42001 governs AI systems and applicable privacy laws govern personal data, so the two regimes operate independently with no expectation of coordination across them.

正解：A

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
ISO/IEC 42001 explicitly integrates data protection and privacy within its governance requirements. Under Clause 6.1.4 (AI system impact assessment), Clause 4.2 (Compliance obligations), and Annex A domain A.7 (Data for AI systems), privacy considerations are recognized as critical inputs to responsible AI management.
AI models trained on personal booking records present significant risks regarding data repurposing, re- identification, model inversion attacks, and non-compliance with data protection legislation (such as GDPR or regional equivalents). Rather than siloing AI governance from data privacy, the standard mandates active coordination. AI risk and impact assessments must evaluate privacy risks, enforce data minimization and pseudonymization, and document lawful processing baselines within the AIMS. Treating privacy as completely isolated from AI oversight or asserting unilateral exemptions for training activities directly violates the standard.
Reference: ISO/IEC 42001:2023 Clause 6.1.4; Control Domain A.7; Annex B.6 (Privacy); GAICC Foundation Module 7 (Data Protection, Privacy, and Model Lifecycle Governance).

質問 # 34
A logistics company has implemented an AI system that recommends staffing levels for warehouse shifts. The user interface shows each recommendation with an ' Approve ' button. In practice, shift managers approve every recommendation within seconds because the system rarely surfaces context, and the interface does not let managers see the inputs that drove the decision. Reviewing this for responsible-AI conformance, which observation BEST reflects the human-oversight principle from ISO/IEC 42001 Annex B?

A. The approve-button workflow satisfies human-oversight expectations because a human is in the loop for every staffing decision, and the speed of approvals reflects the efficiency gains of well-designed AI tooling rather than a control weakness in the deployed interface.
B. The oversight is not meaningful because managers lack the information and context needed to challenge or override the recommendation, so the design provides the appearance of human control without the substance Annex B ' s human-oversight principle expects.
C. Human oversight should be removed in favour of fully automated approvals on grounds of consistency, with sample audits performed monthly by the operations team to verify that the system ' s recommendations remain reasonable across the warehouse network over time.
D. Human oversight is not engaged here because shift staffing is a low-risk operational decision; Annex B
' s oversight principle applies primarily to high-risk AI systems such as those that affect safety or fundamental rights, so the interface design issue is immaterial.

正解：B

解説：
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Annex B of ISO/IEC 42001 and control domain A.8 delineate that human oversight must be substantive and effective rather than superficial or performative. Simply inserting an " Approve " button into a user interface creates an illusion of human-in-the-loop control, especially when human reviewers succumb to automation bias due to lack of contextual input data, rationale transparency, and adequate review time. Under the standard's human agency and oversight principles, supervisory personnel must be equipped with intelligible operational context, actionable confidence scores, and accessible input telemetry to exercise genuine independent judgment, validate outputs, and execute overrides when necessary. Superficial approval workflows fail the governance criteria established by ISO/IEC 42001 for responsible algorithmic deployment.
Reference: ISO/IEC 42001:2023 Annex B.4 (Human oversight); Annex A.8 (AI system operations); GAICC Foundation Module 6 (Human-in-the-Loop & Oversight Architectures).

質問 # 35
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