PECB ISO/IEC 42001:2023 Artificial Intelligence Management System Lead Auditor ISO-IEC-42001-Lead-Auditor Dumps in PDF

Free PECB ISO-IEC-42001-Lead-Auditor Real Questions (page: 12)

[Fundamental Principles and Concepts of an AI Management System] A healthcare provider wants to develop a system that can analyze medical images, such as X-rays and MRIs, to assist doctors in diagnosing diseases.
Which AI concept is most relevant for this application?

  1. Natural Language Processing (NLP)
  2. Computer Vision
  3. Machine Learning (ML)
  4. Deep Learning (DL)

Answer(s): B



[Preparing an ISO/IEC 42001 Audit]
Why is it important to have a clear and agreed audit scope?

  1. To reduce the time required for the audit
  2. To prevent any legal liabilities
  3. To maintain confidentiality of audit findings
  4. To ensure all aspects of the management system are audited

Answer(s): D



[Fundamental Principles and Concepts of an AI Management System] Which core element focuses on ensuring that the creators and operators of AI systems are responsible for the outcomes and impacts of those systems?

  1. Safety and Reliability
  2. Privacy and Security
  3. Accountability
  4. Fairness and Non-Discrimination

Answer(s): C



[Fundamental Principles and Concepts of an AI Management System] Which core element of AIMS is defined as: "Organizations are responsible for the development, deployment, and use of AI systems, and their potential impacts"?

  1. Accountability
  2. Responsibility
  3. Commitment
  4. None of the above

Answer(s): A



[Fundamental Audit Concepts and Principles]
An audit team member is tasked with evaluating a sophisticated AI system used for autonomous driving. They lack the necessary expertise but proceed without consulting a specialist.
Which principle is being neglected in this scenario?

  1. Confidentiality
  2. Independence
  3. Integrity
  4. Due Professional Care

Answer(s): D



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AI Tutor Explanation
8/3/2026 4:40:02 AM

Question 2:
For Question 2, the correct concept is C) Machine Learning (ML).

  • Reason: Predicting future buying behavior from browsing history and past purchases is a predictive analytics task best handled by ML, which learns patterns from historical data.
  • Why not NLP or Computer Vision: NLP is for text data; Computer Vision is for images/videos; neither directly fits predicting customer behavior from structured user data.
  • About Deep Learning: DL is a subset of ML, useful for very large or unstructured data; but for typical retail prediction with structured data, general ML approaches are more appropriate and interpretable.

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