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

Section	Weight	Objectives
Topic 1: ISO/IEC 42001 Requirements and Clauses	45%	- Improvement, nonconformity, and corrective action - Planning, AI risk assessment, and risk treatment - Context of the organization and understanding stakeholder needs - Support, resources, competence, awareness, and communication - Performance evaluation, monitoring, internal audit, and management review - Operation, operational planning, and control - Leadership, commitment, and AI policy
Topic 2: AI Management System Controls and Guidance	30%	- Data governance and lifecycle management for AI systems - Overview of Annex A controls - Responsible AI use, transparency, and accountability
Topic 3: Introduction to Artificial Intelligence and ISO/IEC 42001	25%	- Purpose and benefits of an Artificial Intelligence Management System (AIMS) - Structure and high-level concepts of ISO/IEC 42001 - Overview of Artificial Intelligence concepts and terminology

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q14-Q19):

NEW QUESTION # 14
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. 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.
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. 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.

Answer: D

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 # 15
An AIMS project team is drafting the interested-parties register required under Clause 4.2. A team member suggests limiting the register to paying customers and regulators. The compliance lead pushes back, citing the breadth of interested parties expected under GAICC Foundation Module 4. Which option BEST captures the range of interested parties Clause 4.2 expects?

A. Top management and members of the AI governance committee, since interested parties under Clause
4.2 refers to the internal leadership group accountable for the AIMS.
B. External certification body auditors and the accreditation body, since interested parties under Clause 4.2 captures the assurance ecosystem that audits the AIMS.
C. Customers and competent regulatory authorities, since interested parties under Clause 4.2 are deliberately restricted to entities with formal commercial or statutory relationships.
D. A broad range that includes customers, regulators, employees, data subjects, suppliers, the public, and communities affected by the organisation ' s AI activities.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
ISO/IEC 42001 Clause 4.2 establishes a broad mandate for stakeholder identification that aligns with the pervasive, societal impact of artificial intelligence. Unlike traditional technical standards that focus narrowly on immediate clients and statutory authorities, an AIMS must account for a wide continuum of interested parties. This scope encompasses direct customers, regulatory bodies, internal staff, external suppliers, end- users, individuals whose personal data or intellectual property is ingested for model training, and wider communities or vulnerable groups that can be impacted by automated outputs and classifications. Failing to incorporate non-contractual parties-such as individuals evaluated by automated screening tools-creates blind spots in risk assessment, violating the intent of Clauses 4.2, 6.1.2, and 6.1.4.
Reference: ISO/IEC 42001:2023 Clause 4.2; GAICC Foundation Module 4 (Understanding Stakeholders and Expectations).

NEW QUESTION # 16
A new compliance analyst is reading ISO/IEC 42001 for the first time and asks her mentor what the acronym ' AIMS ' stands for in the context of this standard. According to Clause 3 and GAICC Foundation Module 3, which option provides the correct expansion?

A. AI Monitoring and Surveillance-the runtime telemetry layer that watches deployed AI models for performance drift, anomalous outputs, and integrity breaches.
B. Audit Integrity Maintenance Schedule-the calendar that governs internal audit cadence, surveillance audits, and recertification audits across a three-year cycle.
C. AI Impact Mitigation Strategy-the documented plan describing how an organisation will treat residual AI risks identified through impact assessment activities.
D. AI Management System-the set of interrelated elements an organisation uses to establish policies and objectives for its AI activities and the processes to achieve them.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
In accordance with ISO/IEC 42001 Clause 3 (Terms and definitions) and the Harmonized Structure common to all modern ISO management system standards, the acronym " AIMS " formally stands for " Artificial Intelligence Management System. " Clause 3 defines a management system as a set of interrelated or interacting elements of an organization to establish policies and objectives, as well as processes to achieve those objectives. Therefore, the AIMS encompasses the governance framework, policies, structural controls, operational lifecycles, risk management methodologies, impact assessment mechanisms, and continuous improvement loops established to direct, govern, and control an entity's AI-related undertakings. It must not be conflated with point-in-time runtime telemetry platforms (AI monitoring), risk treatment blueprints, or audit scheduling timelines.
Reference: ISO/IEC 42001:2023 Clause 3.1; GAICC Foundation Module 3 (Vocabulary, Concepts, and Structural Frameworks).
Batch 2: Questions 11 to 20

NEW QUESTION # 17
A foundation candidate is asked to identify which attributes an AI policy must include to satisfy ISO/IEC
42001 Clause 5.2. The candidate is given four short summary statements about an AI policy drafted by a logistics company that uses route-optimisation and demand-forecast models. The policy is one printed page, signed by the chief executive, and posted on the staff intranet. Which summary BEST reflects the minimum attributes Clause 5.2 requires of a documented AI policy?

A. The policy promises to obtain ISO/IEC 42001 certification within twelve months, names the chosen certification body, and commits the chief executive to attend each surveillance audit in person to answer auditor questions.
B. The policy mirrors the wording of the EU AI Act in full, copies the relevant sections of the local privacy statute verbatim, and republishes the text of Annex A controls so staff have one combined reference document.
C. The policy lists every AI model the organisation operates, names the data scientist accountable for each model, and specifies the exact retraining cadence in months for the algorithms used by the logistics planning team.
D. The policy is appropriate to the organisation ' s purpose, provides a framework for AI objectives, commits to satisfying applicable requirements, and commits to continual improvement of the AI management system.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Clause 5.2 (AI Policy) defines four mandatory statutory criteria that top management must satisfy when formulating the documented AI policy. Specifically, the AI policy must: (a) be appropriate to the purpose and context of the organization; (b) provide a structured framework for setting AI objectives; (c) include a formal commitment to satisfy applicable legal, statutory, regulatory, and contractual requirements; and (d) include a clear commitment to the continual improvement of the AI management system. The policy is an executive- level strategic commitment rather than a granular technical asset inventory or model configuration logbook.
Prescribing operational model parameters, copying external legislation verbatim, or committing to prospective audit timelines conflates operational procedures with core leadership governance policies.
Reference: ISO/IEC 42001:2023 Clause 5.2 (AI policy); GAICC Foundation Module 5 (Policy Formulation & Strategic Intent).

NEW QUESTION # 18
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. 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.
C. 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.
D. 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.

Answer: C

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 # 19
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