PECB · ISO42001LI
PECB Certified ISO/IEC 42001 Lead Implementer Practice Tests
Validates the ability to implement and manage an Artificial Intelligence Management System (AIMS) based on ISO/IEC 42001. Covers planning, deploying, evaluating, and continually improving an AIMS within an organization.
Exam Details
Practice Questions
121
≈ 1 practice exams
Duration
180 minutes
Passing Score
70%
Difficulty
ProfessionalLast Updated
Oct 2026
Topics Covered
Fundamental Principles and Concepts of an AI Management SystemAI Management System PlanningAIMS ImplementationSupport and Operation of an AIMSPerformance Evaluation of an AIMSContinual Improvement and AIMS Certification Audit Preparation
PECB Certified ISO/IEC 42001 Lead Implementer Practice Exam Preparation
Use this ISO42001LI practice exam to prepare for PECB Certified ISO/IEC 42001 Lead Implementer with realistic questions, detailed explanations, and focused study modes. The practice bank includes 121 questions for PECB ISO42001LI, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Fundamental Principles and Concepts of an AI Management System, AI Management System Planning, AIMS Implementation, Support and Operation of an AIMS, and Performance Evaluation of an AIMS. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are written for candidates preparing for PECB certification questions and reviewing how the concepts apply in practice. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Sample Questions
5 sample questions with answers and explanations. The full bank has 121 questions, enough for 1 full-length practice exams.
Preview — answers shown1. A team member asks which ISO standard provides the normative reference for AI terminology used in ISO/IEC 42001. Which standard should they consult? (Select one!)
AISO/IEC 22989:2022
BISO/IEC 23894:2023
CISO/IEC 27001:2022
DISO/IEC 42005:2025
Explanation
ISO/IEC 22989:2022 is the normative reference for ISO/IEC 42001, establishing the terminology and concepts for AI. ISO/IEC 23894 provides guidance on AI risk management. ISO/IEC 42005 provides methodology for impact assessments. ISO/IEC 27001 is for information security management. Therefore, 22989 is the correct source for shared language and definitions.
2. A certification body issues a certificate after Stage 2. One year later, an auditor returns to check if the AIMS is still conforming. What type of audit is this? (Select one!)
ARecertification audit
BInternal audit
CSurveillance audit
DStage 1 audit
Explanation
Surveillance audits are conducted annually (at 12-month intervals) to ensure the AIMS continues to conform to the standard and is effectively implemented after initial certification. Recertification audits occur every three years to renew the certificate and are more comprehensive, similar to the initial audit. Stage 1 is a preliminary documentation review before initial certification, and internal audits are conducted by the organization itself, not the external certification body.
3. During a gap analysis, the team identifies that their current processes do not address AI-specific risks like bias. Which ISO standard provides guidance on managing these risks? (Select one!)
AISO/IEC 23894
BISO/IEC 27001
CISO/IEC 42006
DISO/IEC 22989
Explanation
ISO/IEC 23894 provides guidance on AI-specific risk management, extending general risk principles to address AI risks such as bias, opacity, and model drift. ISO/IEC 22989 provides terminology. ISO/IEC 42006 sets requirements for certification bodies. ISO/IEC 27001 focuses on information security. Therefore, 23894 is the appropriate reference for AI risk management guidance.
4. A risk manager is assessing an AI system that uses a complex model where the internal workings are opaque to domain experts. Which term best describes this type of model? (Select one!)
AGlass-box model
BWhite-box model
CBlack-box model
DTransparent model
Explanation
Black-box models are difficult to explain even to domain experts due to their complex internal structures, such as deep neural networks. White-box and glass-box models are designed to be transparent and explainable. Transparent models allow users to understand the decision process, which is the opposite of the opaque system described.
5. After implementing controls to mitigate a risk of biased AI outputs, the team must determine if the remaining risk is acceptable. What is this step called? (Select one!)
AEvaluating risk criteria
BAccepting residual risk
CAnalyzing inherent risk
DIdentifying new risks
Explanation
After risk treatment, the organization must determine if the residual risk (the risk remaining after controls are applied) is acceptable. If the residual risk is still above the risk acceptance criteria, further treatment or escalation is required. This step ensures that the implemented controls have effectively reduced the risk to an acceptable level, distinct from the initial analysis of inherent risk or the evaluation against criteria.
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