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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%	- Key clauses and requirements of ISO/IEC 42001 1. Leadership, planning and support 2. Operation, performance evaluation and improvement
Topic 2: Fundamentals of AI Governance and the ISO/IEC 42001 Framework	40%	- Basic concepts of AI and AI Management Systems (AIMS) 1. Purpose, scope and structure of ISO/IEC 42001:2023 2. Terminology from ISO/IEC 22989 and ISO/IEC 42001 3. AI system lifecycle concepts - Relationship between AIMS and organizational context
Topic 3: Responsible AI, Ethics, and Stakeholder Awareness	20%	- Ethical principles and AI responsibility 1. Roles and responsibilities of AI providers, producers and customers 2. Fairness, explainability and oversight mechanisms 3. Stakeholder engagement and transparency

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q20-Q25):

NEW QUESTION # 20
A government agency is establishing AIMS roles for its first ISO/IEC 42001 implementation. The chief information officer proposes that the AI Ethics Committee, made up of subject-matter experts drawn from across business units, owns AIMS performance reporting. The chief legal officer suggests the role sits with the procurement team because most AI tools are vendor-supplied. A foundation learner is asked what Clause
5.3 actually requires for the reporting-on-performance responsibility. Which response is BEST?

A. Reporting on AIMS performance should sit with the procurement team, because vendor-supplied AI tools dominate the agency ' s portfolio and procurement holds the contractual levers needed to manage supplier obligations through purchase agreements.
B. Reporting on AIMS performance can rest with the AI Ethics Committee in its current advisory form, since cross-functional expertise is well placed to interpret AI risk and benefit signals across the agency ' s varied business areas and to provide nuanced commentary.
C. Reporting on AIMS performance should be split between the AI Ethics Committee and the procurement team on a shared-accountability basis, with each function presenting its segment of performance data to top management at separate review meetings each quarter.
D. Top management must assign the responsibility and authority for reporting on the performance of the AIMS to a specific role or roles, and that assignment must be communicated within the organisation so the reporting line is unambiguous.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Clause 5.3 (Organizational roles, responsibilities and authorities) requires top management to ensure that responsibilities and authorities for reporting on the performance of the AI management system to top management are formally assigned and communicated throughout the organization. The standard intentionally does not dictate the organizational title or functional department where this role must reside-it can be assigned to an AI Ethics Officer, Chief AI Officer, Head of Compliance, or a dedicated steering committee lead-provided the assignment is formally defined, backed by appropriate authority, and clearly communicated to eliminate ambiguity. Leaving governance reporting dispersed across uncoordinated committees or shared informally across multiple departments without clear ownership creates organizational blind spots, conflicting reporting streams, and accountability gaps.
Reference: ISO/IEC 42001:2023 Clause 5.3 (Organizational roles, responsibilities and authorities); GAICC Foundation Module 5 (Structural Roles, Reporting Lines, and Operational Authority).
Batch 4: Questions 31 to 40

NEW QUESTION # 21
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 an authoritative third party that assesses and formally recognises the competence of certification bodies to audit against standards such as ISO/IEC 42001.
C. An accreditation body is the publisher of an ISO standard, responsible for drafting the requirements text, maintaining revisions, and updating the harmonised structure terms.
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.

Answer: B

NEW QUESTION # 22
A telecommunications operator is choosing between two architectures for a customer-service AI assistant.
Option A retrains a very large foundation model weekly on internal data; Option B fine-tunes a substantially smaller model monthly. Both meet functional accuracy requirements. The AIMS lead is asked how ISO/IEC
42001 treats environmental considerations when an organisation selects an AI design. Which response BEST reflects the standard ' s intent?

A. Environmental impact is among the considerations the AI System Impact Assessment is expected to address, so the operator should evaluate the energy and compute footprint of each option alongside functional performance before approving an architecture.
B. Environmental impact considerations apply solely to AI systems that perform climate-related functions such as emissions forecasting; for a customer service assistant the environmental dimension of architecture choice falls outside the relevant scope.
C. Environmental considerations sit outside the AIMS and belong with the corporate sustainability team, so the AI architecture decision should be driven purely by functional accuracy and time-to-market without consideration of energy or compute footprint.
D. Environmental impact should be evaluated by the cloud provider rather than the operator, since the provider operates the underlying infrastructure and is better placed than the operator to compare the relative carbon profiles of competing architectures.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
ISO/IEC 42001 establishes an operational framework that integrates societal and environmental dimensions directly into the engineering and procurement lifecycle of AI systems. Under Clause 6.1.4 (AI System Impact Assessment) and the reference controls in Annex A (specifically domain A.6 regarding resource management and AI lifecycle governance), organizations must assess the broader ramifications of system deployment. This includes evaluating the compute intensity, power consumption, data storage volume, and resulting carbon footprint of competing model designs. An organization cannot abdicate environmental due diligence to a hyperscaler or corporate sustainability committee; the AIMS requires technical decision-makers to evaluate whether excessive computational consumption is disproportionate to functional performance gains.
Comparing retraining cadences and model parameter scale against sustainable resource objectives directly supports responsible AI development and lifecycle optimization.
Reference: ISO/IEC 42001:2023 Clause 6.1.4 (AI system impact assessment); Annex A.6; GAICC Foundation Module 7 (AI Impact Assessment & Societal Considerations).

NEW QUESTION # 23
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 38507, which sets governance implications of AI use for governing bodies and supports board- level oversight in organisations adopting ISO/IEC 42001.
B. ISO/IEC 27001, which sets requirements for information security management systems and is widely used by SaaS vendors that also pursue ISO/IEC 42001.
C. ISO/IEC 22989, which provides AI vocabulary and concepts and is the normative reference cited inside ISO/IEC 42001 for terminology used during audits.
D. ISO/IEC 17021-1, which sets requirements for bodies providing audit and certification of management systems and applies to ISO/IEC 42001 certification bodies.

Answer: D

Explanation:
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).

NEW QUESTION # 24
A retail bank is preparing to deploy a new AI tool that ranks loan applicants by predicted creditworthiness.
The model was trained on five years of historical lending data drawn from a single metropolitan region.
Before go-live, the AIMS lead must propose a fairness check consistent with ISO/IEC 42001 expectations on responsible AI. Which approach BEST reflects the standard ' s intent for bias detection during AI system impact assessment?

A. Document an assumption in the AI System Impact Assessment that historical lending data is representative of the wider applicant population and proceed to deployment, with no quantitative fairness testing of model outputs prior to release.
B. Restrict the deployment to a single business unit for the first month, with no formal pre-launch fairness testing, and rely on customer complaints to surface unfair outcomes for downstream investigation by the responsible model team.
C. Wait for the first quarter of live decisions to accumulate, then run a retrospective fairness analysis on actual decision outcomes, since live data is more representative than any test data the bank could realistically assemble in a development environment.
D. Evaluate predicted-outcome disparities across protected groups using pre-defined fairness metrics, document the results in the AI System Impact Assessment, and confirm acceptance criteria are met before approving the model for deployment.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
In algorithmic lending, bias poses significant legal, regulatory, and ethical risks. Under ISO/IEC 42001 Clause 6.1.4 and Annex A.7 (Data for AI systems), organizations deploying consequential decision-making tools must institute rigorous pre-deployment evaluation processes. Because historical datasets frequently encode institutional disparities and regional sampling imbalances, the organization must establish clear, quantifiable fairness acceptance criteria (such as disparate impact ratios, demographic parity, or equalized odds metrics). Model performance across relevant demographic and protected classes must be measured against these predefined thresholds. The findings, trade-offs, and mitigation strategies must be documented within the AI System Impact Assessment record before production release. Deferring testing until post-launch complaints emerge or relying on unverified assumptions violates the core risk prevention and impact governance mandates of the standard.
Reference: ISO/IEC 42001:2023 Clause 6.1.4; Annex B.3 (Fairness); Control Domain A.7; GAICC Foundation Module 6 (Algorithmic Fairness and Quantitative Evaluation).

NEW QUESTION # 25
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