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

Section	Weight	Objectives
Fundamentals of AI Governance and the ISO/IEC 42001 Framework	40%	- Basic concepts of AI and AI Management Systems (AIMS) 1. Purpose, scope and structure of ISO/IEC 42001:2023 2. Terminology from ISO/IEC 22989 and ISO/IEC 42001 3. AI system lifecycle concepts - Relationship between AIMS and organizational context
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
Responsible AI, Ethics, and Stakeholder Awareness	20%	- Ethical principles and AI responsibility 1. Fairness, explainability and oversight mechanisms 2. Stakeholder engagement and transparency 3. Roles and responsibilities of AI providers, producers and customers

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q35-Q40):

NEW QUESTION # 35
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 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.
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 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.
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: A

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 # 36
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. 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.
B. 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.
C. 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.
D. 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.

Answer: D

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 # 37
A logistics firm ' s executive committee is evaluating the business case for adopting ISO/IEC 42001. The CFO is sceptical and asks which benefit is MOST defensibly attributable to implementing a certified AI management system as described in GAICC Foundation Module 1. Which option is the BEST answer?

A. A legal guarantee of indemnity against any civil claims that may arise from harms caused by deployed AI systems within the certified scope of the AIMS.
B. Automatic conformity with the EU AI Act and equivalent global AI legislation, removing the need for separate legal-compliance reviews of AI deployments.
C. Elimination of model bias in deployed AI systems through the application of Annex A controls, regardless of the training data used during development.
D. Demonstrable due diligence and an evidentiary foundation that supports trust with regulators, customers, and the public regarding the organisation ' s AI governance.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Attaining third-party certification against ISO/IEC 42001 delivers demonstrable proof that an organization has instituted rigorous, verifiable management controls across the entire artificial intelligence lifecycle. While standard implementation does not grant civil indemnity, eliminate statutory liability, or guarantee the total technical removal of algorithmic bias, it provides an auditable evidentiary foundation of due diligence.
Implementing the standard demonstrates to enterprise clients, procurement boards, judicial authorities, and statutory regulators that the entity systematically assesses AI impacts, manages data governance, mitigates known risks, maintains human oversight, and drives continuous operational improvement. This verifiable conformity serves as a market differentiator, establishes competitive advantage in commercial bidding, accelerates institutional trust, and directly substantiates compliance posture during regulatory scrutiny.
Reference: ISO/IEC 42001:2023 Introduction; GAICC Foundation Module 1 (Value Proposition, Trust, and Business Benefits of Certification).

NEW QUESTION # 38
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. 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.
C. 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.
D. 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.

Answer: C

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 # 39
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 publisher of an ISO standard, responsible for drafting the requirements text, maintaining revisions, and updating the harmonised structure terms.
B. 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.
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 internal audit function within an organisation that reviews the AIMS against ISO/IEC 42001 before any external certification audit is conducted.

Answer: C

NEW QUESTION # 40
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