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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%	- Relationship between AIMS and organizational context - Basic concepts of AI and AI Management Systems (AIMS) 1. Terminology from ISO/IEC 22989 and ISO/IEC 42001 2. Purpose, scope and structure of ISO/IEC 42001:2023 3. AI system lifecycle concepts
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. Stakeholder engagement and transparency 3. Fairness, explainability and oversight mechanisms

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q15-Q20):

NEW QUESTION # 15
A retail bank ' s procurement team has begun receiving requests from enterprise clients asking the bank to demonstrate how it governs the AI models used in credit decisioning. The Chief Risk Officer wants a single, internationally recognised framework that addresses AI specifically-not just general quality or information- security controls-and that can be independently certified. Which standard BEST fits this need under the GAICC Foundation view of why ISO/IEC 42001 was published?

A. ISO 9001, the international quality management system standard, which already covers AI quality assurance for regulated banking workloads.
B. The OECD AI Principles, which provide an internationally agreed policy framework for trustworthy AI and a basis for third-party certification.
C. ISO/IEC 27001, the information security management system standard, which fully addresses AI governance through its existing Annex A control set.
D. ISO/IEC 42001, the first international management system standard dedicated to artificial intelligence and capable of third-party certification.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
ISO/IEC 42001 was developed jointly by ISO and IEC (through JTC 1/SC 42) to establish the world's first certifiable Artificial Intelligence Management System (AIMS) standard. While ISO 9001 addresses overarching quality processes and ISO/IEC 27001 governs information security, neither framework provides specialized, auditable criteria for unique AI risks, including model hallucinations, statistical bias, algorithmic drift, lack of explainability, data poisoning, and autonomous decision opacity. Furthermore, while the OECD AI Principles define high-level global policy benchmarks for trustworthy AI, they are non-binding policy guidance that lack formal conformity assessment specifications. ISO/IEC 42001 fills this gap by delivering an auditable, Harmonized Structure management system against which accredited conformity assessment bodies can perform independent third-party audits and issue formal organizational certification.
Reference: ISO/IEC 42001:2023 Scope; GAICC Foundation Module 1 (The Emergence & Global Role of ISO
/IEC 42001).

NEW QUESTION # 16
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 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.
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. 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.
D. 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.

Answer: C

Explanation:
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).

NEW QUESTION # 17
An insurer deploys an AI model that supports underwriter decisions on personal-lines policies. Three audiences need explanations: applicants who receive a decline, internal underwriters who supervise the model
' s outputs, and an external regulator conducting a market review. A foundation learner is asked how ISO/IEC
42001 expects explainability to be approached across these audiences. Which response BEST reflects the responsible-AI principle of explainability?

A. Tailor the explanation to each audience: plain-language reasoning for the declined applicant, feature- level rationale for the underwriter, and methodology and validation evidence for the regulator-each calibrated to its purpose.
B. Provide explanations to the regulator and the underwriters but withhold any explanation from declined applicants on commercial-sensitivity grounds, so that proprietary aspects of the underwriting approach remain protected from competitor scrutiny.
C. Provide a single technical explanation document, copied verbatim from the model-development team, to each of the three audiences, on the basis that a uniform explanation supports consistency and auditability across stakeholders interacting with the system.
D. Defer explainability entirely until a complaint or regulatory enquiry is raised, then assemble an explanation reactively for the specific case, since pre-built explanations may not address the particular query the requesting party actually puts to the insurer.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Explainability under ISO/IEC 42001 Annex B and Annex A domain A.8 is not a one-size-fits-all technical readout. The standard explicitly recognizes that different interested parties require information delivered at different levels of abstraction to make the explanation meaningful and actionable. For consumers receiving an adverse outcome (such as a declined policy), explainability requires plain-language reasoning detailing the principal factors and criteria that led to the determination, enabling individual understanding and potential recourse. For internal operators (underwriters), explainability demands feature-importance scores, localized attribution weights, and confidence thresholds to enable effective human oversight and validation. For supervisory regulators, explainability requires comprehensive mathematical methodology disclosures, architectural validation reports, data provenance verification, and fairness audit records. Calibrating explanations to stakeholder contexts reflects the core principles of transparency, accountability, and user- centric governance.
Reference: ISO/IEC 42001:2023 Annex B.5 (Transparency and explainability); Control Domain A.8 (AI system operations); GAICC Foundation Module 6 (Calibrated Explainability and Stakeholder Communication).

NEW QUESTION # 18
During a Foundation classroom exercise, learners are asked to sort four short documents into the categories defined by ISO/IEC 42001. One document is a one-page statement signed by the chief executive that sets direction for responsible AI use, commits to applicable requirements, and commits to continual improvement of the AIMS. Three other documents describe controls, model training procedures, and an incident log. Which categorisation of the signed one-page statement is BEST aligned with Clause 5.2?

A. It is a management-review output captured under Clause 9.3, since the chief executive has signed it and the document expresses commitments at a strategic level appropriate to a periodic review of the management system.
B. It is an Annex A control implementation record, since it describes how the organisation intends to apply selected controls drawn from the Annex A reference set during the operational phase of the management system.
C. It is an AI system impact assessment summary, because it sets out the organisation ' s intended position on responsible AI use at a level that informs the impact-assessment scoring rubric for individual systems.
D. It is the AI policy required by Clause 5.2, because top management has documented direction for AI, set a framework for objectives, and committed to satisfying applicable requirements and to continual improvement.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
The signed executive document fulfills all core statutory requirements specified in ISO/IEC 42001 Clause 5.2 (AI Policy). Under Clause 5.2, top management must establish an AI policy that is appropriate to the purpose of the organization, provides a framework for setting AI objectives, includes a commitment to satisfy applicable requirements, and includes a commitment to continual improvement of the AIMS. A formal policy is an overarching governance instrument demonstrating leadership intent, unlike operational procedures (which govern algorithmic training, testing, or incident response), impact assessments (which analyze discrete systems), or control implementation records (which document technical mitigations). Furthermore, while management review minutes (Clause 9.3) contain strategic decisions and resource actions, the baseline governance commitments defined here represent the foundational policy instrument itself.
Reference: ISO/IEC 42001:2023 Clause 5.2 (AI policy); GAICC Foundation Module 5 (Governance Instruments and Policy Documentation).

NEW QUESTION # 19
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 technical performance benchmark comparing model accuracy across vendor offerings, used to select the most suitable AI model for production deployment.
C. A formal process for identifying and evaluating the potential consequences of an AI system on individuals, groups, and society, including affected interested parties.
D. A financial cost-benefit analysis comparing the total cost of ownership of competing AI platforms during procurement, including licence and infrastructure costs.

Answer: C

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 # 20
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