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

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

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q26-Q31):

NEW QUESTION # 26
A small insurance firm is performing its first context analysis under Clause 4.1 of ISO/IEC 42001. The compliance lead lists the factors the team should consider. Which item BEST illustrates an EXTERNAL issue relevant to the AIMS, consistent with GAICC Foundation Module 4?

A. The forthcoming enforcement of regional AI legislation, such as the EU AI Act, and evolving public expectations about transparency in automated insurance decisions.
B. The composition of the firm ' s senior leadership team and the way responsibilities for AI initiatives are currently allocated across internal business units.
C. The firm ' s internal organisational culture and the maturity of its data engineering team, both of which influence how AI projects are scoped and delivered.
D. The current inventory of AI models deployed in the underwriting platform and the technical debt accumulated within the firm ' s own internal data pipelines over time.

Answer: A

NEW QUESTION # 27
An IT director is finalising the certification scope statement for his organisation ' s AIMS. He has been told that ISO/IEC 42001 is built on the Harmonized Structure used by other ISO management system standards.
According to GAICC Foundation Module 2, which clauses of ISO/IEC 42001 contain the auditable management system requirements?

A. Clauses 1, 2, and 3, which together set scope, normative references, and terms-the binding requirements that an audit team checks for conformity.
B. Clauses 4 through 10, which carry the management system requirements that an external audit assesses for conformity against the standard.
C. Annexes A, B, C, and D, which are the principal sources of audit findings during a Stage 2 certification audit against an ISO management system standard.
D. Clause 6 by itself, since it covers planning, risk assessment, and AI System Impact Assessment, which are the AI-specific elements that auditors evaluate.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Like all modern ISO management system standards developed under ISO/IEC Directives Part 1 (such as ISO
9001, ISO/IEC 27001, and ISO 14001), ISO/IEC 42001 utilizes the standardized Harmonized Structure (formerly High-Level Structure / Annex SL). Within this universal structure, Clauses 1, 2, and 3 provide introductory context, normative references, and formal terminology definitions, respectively; they do not contain auditable management system requirements. The formal, auditable requirements against which external conformity assessment bodies evaluate an organization reside exclusively within Clauses 4 through
10: Context of the organization (4), Leadership (5), Planning (6), Support (7), Operation (8), Performance evaluation (9), and Improvement (10). While Annex A supplies reference controls assessed via the Statement of Applicability under Clause 6.1.3, the primary audit criteria are embedded within the core requirements of Clauses 4 through 10.
Reference: ISO/IEC 42001:2023 Clauses 4-10; GAICC Foundation Module 2 (Harmonized Structure & Standard Architecture).

NEW QUESTION # 28
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 periodic vulnerability scan of AI infrastructure used to identify security weaknesses in model-serving environments, container images, and supporting APIs.
B. A formal process for identifying and evaluating the potential consequences of an AI system on individuals, groups, and society, including affected interested parties.
C. A financial cost-benefit analysis comparing the total cost of ownership of competing AI platforms during procurement, including licence and infrastructure costs.
D. A technical performance benchmark comparing model accuracy across vendor offerings, used to select the most suitable AI model for production deployment.

Answer: B

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 # 29
A foundation workshop case describes a small financial-advisory firm that has just signed its AI policy. The compliance officer asks what the firm should do next to translate the policy into operational practice for a new portfolio-recommendation AI tool the firm is preparing to deploy. Which next step BEST reflects how ISO
/IEC 42001 expects policy to connect to planning and operations for a specific AI system?

A. Conduct an AI risk assessment under Clause 6.1.2 and an AI System Impact Assessment under Clause
6.1.4 for the portfolio-recommendation tool, then plan risk treatment with controls aligned to the policy' s commitments.
B. Re-sign the AI policy in front of staff at the next quarterly town-hall meeting to demonstrate leadership commitment, on the basis that visible re-signing reinforces the message conveyed by the original signature at policy adoption.
C. Print and laminate the signed policy for display in the office reception area, then distribute copies to each adviser ' s desk so that the document is visible during client meetings held in the firm ' s premises across the next quarter.
D. Defer further action until the firm has accumulated several months of live experience with the portfolio- recommendation tool, on the basis that risk and impact assessments are more accurate when informed by post-deployment operational data points.

Answer: A

NEW QUESTION # 30
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. 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.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 31
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