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

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

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q22-Q27):

NEW QUESTION # 22
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 the internal audit function within an organisation that reviews the AIMS against ISO/IEC 42001 before any external certification audit is conducted.
C. 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.
D. An accreditation body is the publisher of an ISO standard, responsible for drafting the requirements text, maintaining revisions, and updating the harmonised structure terms.

Answer: C

NEW QUESTION # 23
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 # 24
A foundation case workshop closes with a scenario in which a charity is about to launch an AI tool that helps caseworkers prioritise vulnerable-client outreach. The AIMS lead is asked which set of considerations the launch checklist should reflect, drawing on the responsible-AI principles in ISO/IEC 42001 Annex B together with the relevant management-system clauses. Which response BEST integrates the standard ' s expectations for a deployment of this kind?

A. Verify the model ' s predictive accuracy meets the agreed target, document the result in a one-page summary signed by the head of technology, and proceed to launch without further reference to other responsible-AI considerations for this charitable use case.
B. Complete an AI System Impact Assessment under Clause 6.1.4 covering fairness, transparency, human oversight, privacy, and safety considerations, define acceptance criteria, assign role responsibilities, and gain documented approval before launch.
C. Postpone any structured impact assessment until after the first quarter of operational data has been gathered, on the basis that the charity will be better placed to evaluate impact once real-world caseworker usage patterns and outcome distributions have been observed.
D. Confirm that the vendor has provided a model card, treat that document as a complete responsible-AI record for the deployment, and rely on the vendor ' s representations rather than performing an internal review of the model ' s fitness for the charity ' s specific population of clients.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Deploying an algorithm that affects vulnerable individuals requires an end-to-end integration of risk treatment, ethical safeguards, and management system governance. Clause 6.1.4 (AI system impact assessment) mandates that organizations establish, implement, and maintain processes for assessing the potential impacts of the AI system on individuals, groups, and society. In a high-consequence charitable triage scenario, impact assessment cannot be deferred until post-deployment or reduced solely to verifying statistical accuracy. The assessment must evaluate core responsible AI principles outlined in Annex B, including fairness across marginalized subgroups, human oversight to ensure caseworkers retain override discretion, transparency regarding prioritization criteria, data privacy, and unintended harms. Deploying entities must define objective acceptance thresholds, formally assign operational monitoring responsibilities under Clause
5.3, and obtain sign-off before operational launch.
Reference: ISO/IEC 42001:2023 Clause 6.1.4 (AI system impact assessment); Annex B (Guidance on responsible AI principles); GAICC Foundation Module 7 (AI Impact Assessment & Lifecycle Governance).

NEW QUESTION # 25
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. 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.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 26
An education provider uses an AI plagiarism-detection tool supplied by a third-party vendor. A student is flagged incorrectly and appeals. The teacher refers the matter to the IT helpdesk, the helpdesk refers it to the procurement team, procurement refers it to the vendor, and the vendor refers it back to the institution. The student receives no clear response for several weeks. Which issue BEST reflects an accountability weakness under the responsible-AI principles in ISO/IEC 42001?

A. The student should be required to escalate the appeal through a formal external regulator rather than through the institution, as external regulators are better placed than internal staff to adjudicate AI- related complaints raised by individual learners.
B. No specific role inside the institution has been assigned responsibility for AI-system outcomes experienced by users, so the appeal cannot be addressed end to end-a gap inconsistent with the accountability principle and with Clause 5.3 role assignment.
C. Accountability is adequate because each team in the referral chain followed its own internal procedure, and procedural compliance by individual teams demonstrates that responsibility has been appropriately distributed across the institution.
D. The vendor should be considered solely accountable for the misclassification because the model was developed and trained externally, and the institution is therefore a passive consumer of the vendor ' s pre-built classification capability for student submissions.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Accountability is a foundational pillar of ethical AI codified in ISO/IEC 42001 Annex B and enforced operationally through Clause 5.3 and Annex A controls (specifically A.2 on governance and A.9 on third- party relationships). Organizations deploying third-party AI systems cannot outsource accountability for end- user outcomes to commercial suppliers or diffuse responsibility across disjointed internal operational silos.
When an adverse or disputed decision occurs, the deploying entity must provide a defined recourse and human-oversight escalation pathway. The breakdown described illustrates a severe governance void: the lack of a designated role with clear authority to arbitrate model exceptions and resolve appeals. ISO/IEC 42001 mandates that organizations retain clear, end-to-end accountability for any operational system integrated into their service delivery pipelines.
Reference: ISO/IEC 42001:2023 Clause 5.3; Annex B.2 (Accountability); GAICC Foundation Module 6 (Responsible AI Principles & Organizational Accountability).

NEW QUESTION # 27
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