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PECB ISO-IEC-42001-Lead-Auditor Most Reliable Questions & ISO-IEC-42001-Lead-Auditor Valid Exam Review

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PECB ISO-IEC-42001-Lead-Auditor Exam Syllabus Topics:

Topic
Details

Topic 1

Preparing an ISO
IEC 42001 audit: This section of the exam measures the skills of a Lead Auditor and covers how to plan and prepare for an AI management system audit. It includes creating audit plans, selecting team members, and setting clear objectives to ensure a smooth audit process.

Topic 2

Conducting an ISO
IEC 42001 audit: This section of the exam measures the skills of a Lead Auditor and focuses on executing the audit according to ISO
IEC 42001 guidelines. It includes collecting evidence, interviewing relevant staff, and evaluating compliance with the AI management system standards.

Topic 3

Fundamental principles and concepts of an AI management system: This section of the exam measures the skills of an AI Compliance Officer and covers the basic principles of artificial intelligence, including ethical use, trustworthiness, and transparency. It introduces the purpose and importance of having an AI management system in place for responsible AI governance.

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PECB ISO/IEC 42001:2023Artificial Intelligence Management System Lead Auditor Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
Scenario 6 (continued):
Scenario 6: HappilyAI is a pioneering enterprise dedicated to developing and deploying artificial intelligence Al solutions tailored toenhance customer service experiences across various industries. The company offers innovative products like virtual assistants,predictive analytics tools, and personalized customer interaction platforms. As part of its commitment to operational excellence andinnovation, HappilyAI has implemented a robust Al management system AIMS to oversee its Al operations effectively. Currently.HappilyAI is undergoing a comprehensive audit process of its AIMS to evaluate its compliance with ISO/IEC 42001.
Under the leadership of Jess, the audit team began the audit process with meticulous planning and coordination, setting the groundworkfor the extensive on-site activities of the stage 1 audit. This initial phase was marked by a comprehensive documentation review. Theaudit scope encompassed a critical review of HappilyAI's core departments, including Research and Development (R&D), CustomerService, and Data Security, aiming to assess the conformity of HappilyAI's AIMS to the requirements of ISO/IEC 42001.
Afterward, Jess and the team conducted a formal opening meeting with HappilyAI to introduce the audit team and outline the auditactivities. The meeting set a collaborative tone for the subsequentphases, where the team engaged in information collection, executedaudit tests, identified findings, and prepared draft nonconformity reports while maintaining a strict quality review process.
In gathering evidence, the audit team employed a sampling method, which involved dividing the population into homogeneous groups toensure a comprehensive and representative data collection by drawing samples from each segment. Furthermore, the team employedobservation to deepen their understanding of the Al management processes. They verified the availability of essential documentation,including Al-related policies, and evaluated the communication channels established for reporting incidents.
Additionally, they scrutinized specific monitoring tools designed to track the performance of data acquisition processes, ensuring thesetools effectively identify and respond to errors or anomalies. However, a notable challenge emerged as the team encountered a lack ofaccess to documented information that describes how tasks about AIMS are executed. In addition to this, the team identified a potentialnonconformity within the Sales Department. They decided not to record this as a nonconformity in the audit report but onlycommunicated it to the HappilyAI's representatives.
During the stage 2 audit, the certification body, in collaboration with HappilyAI, assigned the roles of technical experts within the auditteam. Recognized for their specialized knowledge and expertise in artificial intelligence and its applications, these technical experts aretasked with the thorough assessment of the AIMS framework to ensure its alignment with industry standards and best practices,focusing on areas such as data ethics, algorithmic transparency, and Al system security.
Question:
Which observation types did the audit team use to enhance their understanding of the AI management processes?

A. General and detailed
B. Qualitative and quantitative
C. Statistical and methodical

Answer: A

Explanation:
The audit team usedGeneral and Detailed observations:
* General observations refer to broad, overall assessments.
* Detailed observations involve in-depth, specific reviews of processes.
* ISO 19011:2018 Clause 6.5.7states:"Auditors may conduct general observations to understand the context, followed by detailed observations to examine specific controls and compliance." Reference:ISO 19011:2018 Clause 6.5.7; ISO/IEC 42001 Lead Auditor Study Manual, Section 6 ("Observation Techniques").

NEW QUESTION # 78
Which step involves reviewing documents and records relevant to the audit scope?

A. Audit reporting
B. Closing meeting
C. Audit follow-up
D. Document review

Answer: D

Explanation:
TheDocument Reviewstep is a key part of audit preparation where auditors evaluate relevantdocuments, records, policies, and proceduresto understand the structure and implementation of the AI Management System.
As perISO 19011:2018 - Clause 6.4.3, document review helps auditorsfamiliarize themselves with the management system, identify potential areas of concern, and refine the audit plan.
In AI audits (such as AIMS under ISO/IEC 42001), this may include reviewingAI governance policies, data governance procedures, impact assessments, or model documentation.
Reference: ISO 19011:2018 - Clause 6.4.3 (Review of documented information) ISO/IEC 42001:2023 - Clause 9.2.1 (Internal audit preparation) PECB Lead Auditor Guide - Domain 4: "Document and Record Review in Audit Planning"

NEW QUESTION # 79
Based on the scenario above, answer the following question:
Which activity conducted during the stage 2 audit does not follow best practices?

A. Conducting interviews with auditee personnel
B. Conducting on-site activities
C. Conducting the opening meeting with the auditee present
D. Skipping the review of documented information related to the AIMS

Answer: D

Explanation:
Even though some documented information was reviewed during Stage 1, ISO/IEC 17021-1:2015 (Clause
9.3.1.2.2) and ISO 19011:2018 (Clause 6.5.2) recommend that auditors should not entirely skip the review of documented information during the Stage 2 audit.
The Stage 2 audit is intended to evaluate the implementation and effectiveness of the management system, and this includes ensuring that documented information is not only available but also maintained, communicated, and used properly in operations.
Skipping this step may lead to overlooking changes made after Stage 1 or gaps not previously identified.
Reference:
ISO/IEC 17021-1:2015, Clause 9.3.1.2.2
ISO 19011:2018, Clause 6.5.2 - Conducting Document Review
PECB ISO/IEC 42001 Lead Auditor Study Guide - Stage 2 Audit Activities

NEW QUESTION # 80
Which of the following examples is frequent analysis?

A. The auditor observes the AI system's performance once during its initial deployment to ensure it meets operational standards
B. The auditor reviews post-project performance reports generated after a two-year AI implementation cycle
C. The auditor selects a sample of employees to determine if they are aware of their roles and responsibilities relevant to AI
D. The auditor conducts a yearly review of the company's financial statements to assess long-term financial stability

Answer: C

Explanation:
Frequent analysis refers to audit or evaluation activities that are performed on a recurring basis to monitor and assess compliance, effectiveness, or awareness. Among the options, selecting a sample of employees to verify awareness of AI-related responsibilities is likely to occur regularly - especially during audits, trainings, or internal assessments - making it an example of frequent analysis.
Option B and D refer to periodic or annual analysis. Option C is a one-time assessment tied to initial deployment, not frequent analysis.
Reference:
ISO/IEC 42001:2023, Clause 9.2 - Internal audits
ISO 19011:2018, Clause 6.5.5 - Use of sampling and recurring review
PECB ISO/IEC 42001 Lead Auditor Guide - Section: Frequency and Methods of Audit Observation
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NEW QUESTION # 81
Scenario: NeuraGen, founded by a team of AI experts and data scientists, has gained attention for its advanced use of artificial intelligence. It specializes in developing personalized learning platforms powered by AI algorithms. MindMeld, its innovative product, is an educational platform that uses machine learning and stands out by learning from both labeled and unlabeled data during its training process. This approach allows MindMeld to use a wide range of educational content and personalize learning experiences with exceptional accuracy. Furthermore, MindMeld employs an advanced AI system capable of handling a wide variety of tasks, consistently delivering a satisfactory level of performance. This approach improves the effectiveness of educational materials and adapts to different learners' needs.
NeuraGen skillfully handles data management and AI system development, particularly for MindMeld.
Initially, NeuraGen sources data from a diverse array of origins, examining patterns, relationships, trends, and anomalies. This data is then refined and formatted for compatibility with MindMeld, ensuring that any irrelevant or extraneous information is systematically eliminated. Following this, values are adjusted to a unified scale to facilitate mathematical comparability. A crucial step in this process is the rigorous removal of all personally identifiable information (PII) to protect individual privacy. Finally, the data is subjected to quality checks to assess its completeness, identify any potential bias, and evaluate other factors that could impact the platform's efficacy and reliability.
NeuraGen has implemented an advanced artificial intelligence management system (AIMS) based on ISO
/IEC 42001 to support its efforts in AI-driven education. This system provides a framework for managing the life cycle of AI projects, ensuring that development and deployment are guided by ethical standards and best practices.
NeuraGen's top management is key to running the AIMS effectively. Applying an international standard that specifically provides guidance for the highest level of company leadership on governing the effective use of AI, they embed ethical principles such as fairness, transparency, and accountability directly into their strategic operations and decision-making processes.
While the company excels in ensuring fairness, transparency, reliability, safety, and privacy in its AI applications, actively preventing bias, fostering a clear understanding of AI decisions, guaranteeing system dependability, and protecting user data, it struggles to clearly define who is responsible for the development, deployment, and outcomes of its AI systems. Consequently, it becomes difficult to determine responsibility when issues arise, which undermines trust and accountability, both critical for the integrity and success of AI initiatives.
What kind of AI system does MindMeld utilize?

A. Strong AI
B. General AI
C. Narrow AI

Answer: C

Explanation:
MindMeld is described as an advanced AI system capable of performing a wide range of tasks within the domain of personalized education, delivering high performance consistently. However, it is still specialized and focused on a specific field - educational content delivery and personalization. This matches the definition of Narrow AI.
Narrow AI (also known as Weak AI) is designed and trained for a particular task or a narrow range of tasks. It may appear highly intelligent in its niche but lacks generalization beyond its scope.
General AI or Strong AI (options B and C) refer to systems with human-like reasoning and the ability to understand, learn, and apply knowledge across a wide range of domains, not just a specific task or industry.
There is currently no commercially deployed General or Strong AI. Therefore, based on the description in the scenario, MindMeld falls under Narrow AI.
Reference:
* ISO/IEC 42001:2023, Clause 4.2 - Understanding the nature and scope of the AI system, including intended purpose, tasks, and context.
* ISO/IEC 22989:2022 (Artificial Intelligence - Concepts and terminology), which defines:
* Narrow AI as AI systems that are designed to perform specific tasks (Clause 3.15)
* General AI (AGI) as theoretical systems with the capacity for general cognitive functions like a human (Clause 3.16)
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NEW QUESTION # 82
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