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

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

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q16-Q21):

NEW QUESTION # 16
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. 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.
C. 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.
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.

Answer: B

Explanation:
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).

NEW QUESTION # 17
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. 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.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 18
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. 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.
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. Public-sector bodies subject to AI legislation, since voluntary management system standards are intended for regulated entities rather than commercial start-ups.
D. 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.

Answer: A

Explanation:
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).

NEW QUESTION # 19
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 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.
B. 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.
C. 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.
D. 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.

Answer: A

Explanation:
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).

NEW QUESTION # 20
A foundation learner is shown four pieces of evidence collected during a Clause 5 walkthrough at a media company that deploys content-moderation AI. The auditor wants the single strongest indicator that top management is actively committed to the AIMS, not merely supportive on paper. The four pieces of evidence are an intranet news item, a printed policy, a budget line, and a signed management-review minute. Which item BEST evidences active Clause 5.1 commitment?

A. A finance-system budget line called " AI governance " totalling a substantial annual figure, with no supporting breakdown of how funds were allocated across training, tooling, monitoring, or external advisory services during the period.
B. An intranet news item from twelve months ago announcing that the organisation has adopted ISO/IEC
42001 as its preferred AI management framework, written by the corporate-communications team rather than by top management directly.
C. A signed management-review minute showing the chief executive chaired the review, reviewed AIMS performance indicators, approved resource changes, and tasked named owners with follow-up actions carrying agreed deadlines and reporting dates.
D. A printed AI policy displayed in the reception lobby, signed by a former chief executive who departed before the AIMS scope was extended to cover the newer generative-AI content-moderation tools that the company now operates.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
In ISO conformity assessment audits, external auditors distinguish between superficial paper compliance and genuine, verifiable leadership commitment under Clause 5.1. While published policies, internal communications, and high-level financial allocations represent supporting evidence, they are insufficient on their own to substantiate direct leadership engagement. A signed management review record (Clause 9.3) demonstrating that the chief executive personally presided over the governance review, critically assessed operational telemetry and audit findings, authorized concrete resource allocations, and assigned actionable remediation tasks to named owners establishes clear, verifiable proof of active leadership oversight. This record links top-level strategic authority directly to operational accountability and continual improvement, providing the primary evidence needed to satisfy Clause 5.1.
Reference: ISO/IEC 42001:2023 Clause 5.1 (Leadership and commitment); Clause 9.3 (Management review); GAICC Foundation Module 5 (Leadership and Top Management Audit Trails).

NEW QUESTION # 21
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