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

Section	Weight	Objectives
Topic 1: 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
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: 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. AI system lifecycle concepts 3. Purpose, scope and structure of ISO/IEC 42001:2023

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GAICC ISO/IEC 42001 Foundation Sample Questions (Q20-Q25):

NEW QUESTION # 20
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 an authoritative third party that assesses and formally recognises the competence of certification bodies to audit against standards such as ISO/IEC 42001.
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 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.
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: A

NEW QUESTION # 21
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. 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.
C. 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.
D. 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.

Answer: B

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 # 22
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 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.
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 composition of the firm ' s senior leadership team and the way responsibilities for AI initiatives are currently allocated across internal business units.

Answer: A

NEW QUESTION # 23
A mid-sized SaaS company has implemented its AIMS and now wants to obtain certification against ISO/IEC
42001. The CISO asks which standard governs the bodies that will conduct the external certification audit, so she can check the credentials of the shortlisted firms. Which standard is the right reference?

A. ISO/IEC 22989, which provides AI vocabulary and concepts and is the normative reference cited inside ISO/IEC 42001 for terminology used during audits.
B. ISO/IEC 38507, which sets governance implications of AI use for governing bodies and supports board- level oversight in organisations adopting ISO/IEC 42001.
C. ISO/IEC 27001, which sets requirements for information security management systems and is widely used by SaaS vendors that also pursue ISO/IEC 42001.
D. ISO/IEC 17021-1, which sets requirements for bodies providing audit and certification of management systems and applies to ISO/IEC 42001 certification bodies.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From [GAICC ISO/IEC 42001/Course Guide/topics]:
Conformity assessment bodies (CABs) that evaluate organizations for certification against management system standards must themselves satisfy the stringent competence and operational criteria set out in ISO/IEC
17021-1 (Conformity assessment - Requirements for bodies providing audit and certification of management systems). ISO/IEC 17021-1 establishes the global benchmark for impartiality, audit methodology, auditor qualifications, and quality control that certification bodies must demonstrate to national accreditation bodies (such as UKAS, ANAB, or DAkkS). While ISO/IEC 22989 is the foundational terminology standard and ISO
/IEC 38507 provides corporate governance guidance for boards, neither standard governs the operations of third-party certification bodies. Checking that a certification body holds accreditation under ISO/IEC 17021-1 ensures that the resulting certificate is credible, verifiable, and internationally recognized.
Reference: ISO/IEC 17021-1; GAICC Foundation Module 2 (The Conformity Assessment Ecosystem & Third-Party Auditing).

NEW QUESTION # 24
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 formal process for identifying and evaluating the potential consequences of an AI system on individuals, groups, and society, including affected interested parties.
B. A financial cost-benefit analysis comparing the total cost of ownership of competing AI platforms during procurement, including licence and infrastructure costs.
C. A periodic vulnerability scan of AI infrastructure used to identify security weaknesses in model-serving environments, container images, and supporting APIs.
D. A technical performance benchmark comparing model accuracy across vendor offerings, used to select the most suitable AI model for production deployment.

Answer: A

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 # 25
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