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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: 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
Topic 3: Fundamentals of AI Governance and the ISO/IEC 42001 Framework	40%	- Relationship between AIMS and organizational context - Basic concepts of AI and AI Management Systems (AIMS) 1. AI system lifecycle concepts 2. Purpose, scope and structure of ISO/IEC 42001:2023 3. Terminology from ISO/IEC 22989 and ISO/IEC 42001

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

NEW QUESTION # 20
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 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.
B. 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.
C. 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.
D. 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.

Answer: D

Explanation:
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).

NEW QUESTION # 21
A new AIMS coordinator is asked to summarise the difference between an AI system and an AI System Impact Assessment (AI-SIA) for an internal awareness page. According to ISO/IEC 42001 Clause 3 and GAICC Foundation Module 3, which option BEST captures what an AI-SIA is?

A. A formal process for identifying and evaluating the potential consequences of an AI system on individuals, groups, and society, including affected interested parties.
B. A periodic vulnerability scan of AI infrastructure used to identify security weaknesses in model-serving environments, container images, and supporting APIs.
C. A technical performance benchmark comparing model accuracy across vendor offerings, used to select the most suitable AI model for production deployment.
D. A financial cost-benefit analysis comparing the total cost of ownership of competing AI platforms during procurement, including licence and infrastructure costs.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Under ISO/IEC 42001 Clause 3 and Clause 6.1.4, an AI System Impact Assessment (AI-SIA) is defined as a systematic process to identify, analyze, and evaluate the potential consequences of an AI system on individuals, groups of individuals, society, and the environment. While conventional enterprise risk management (Clause 6.1.2) evaluates risks primarily from the perspective of potential harm to the organization itself (financial, operational, reputational, legal), the AI-SIA adopts an outward-facing focus on external stakeholders and fundamental societal rights. The AI-SIA examines considerations such as demographic bias, discriminatory outcomes, threats to user autonomy, privacy degradation, and broader societal impacts. It is neither a hardware benchmark, a vendor total-cost-of-ownership model, nor a runtime cybersecurity vulnerability scan. It is a structured governance methodology for ensuring ethical and legal accountability throughout the AI lifecycle.
Reference: ISO/IEC 42001:2023 Clause 3 & Clause 6.1.4; GAICC Foundation Module 3 (Vocabulary, Concepts, and System Definitions).

NEW QUESTION # 22
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. 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.
B. 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.
C. 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.
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.

Answer: C

Explanation:
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).

NEW QUESTION # 23
A consultancy is helping a healthcare provider plan its ISO/IEC 42001 certification scope. The provider operates many business units but wants to certify only the AIMS covering its radiology AI triage service. The auditor warns that any exclusion from the certification scope must meet a specific expectation described in GAICC Foundation Module 2 and Clause 4.3. Which statement BEST captures that expectation?

A. Exclusions are decided exclusively by the certification body during the Stage 1 audit, based on a sampling review of the organisation ' s AI inventory and risk register.
B. Exclusions must be supported by a documented justification linked to context, interested parties, and AI roles, rather than being stated without rationale in the scope statement.
C. Exclusions are not permitted under ISO/IEC 42001, so the entire organisation must be brought into the certification scope before a Stage 1 audit can commence.
D. Exclusions may be declared at the discretion of top management and recorded in meeting minutes, with no further documentation required for the external certification audit.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Under ISO/IEC 42001 Clause 4.3, an organization has the architectural freedom to define its certification boundaries to cover the entire enterprise, specific operational divisions, or individual AI products and services (such as a radiology triage tool). However, scoping is not arbitrary. The standard explicitly requires that when an organization narrows its scope or excludes specific operational activities, business units, or AI systems, every exclusion must be explicitly documented and supported by a robust, defensible justification. This rationale must demonstrate that exclusions do not compromise the organization ' s ability to satisfy applicable requirements, evade relevant AI risks, or ignore the legitimate expectations of affected interested parties identified under Clause 4.2. An unsupported exclusion will be challenged as an audit finding during Stage 1 conformity assessments.
Reference: ISO/IEC 42001:2023 Clause 4.3 (Determining the scope of the AI management system); GAICC Foundation Module 2 (Scoping Principles, Boundaries, and Exclusions).
Batch 3: Questions 21 to 30

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. 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.
D. 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.

Answer: C

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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