PECB ISO-IEC-42001-Lead-Auditor Exam With Confidence Using Practice Dumps

Machine Learning (ML) is the most suitable AI concept in this scenario. ML focuses on developing algorithms that can learn from structured or unstructured data and make predictions based on historical patterns.

In this case, analyzing customer browsing history and purchase records falls directly under supervised learning, a subcategory of ML, which is typically used for predictive modeling in retail (such as next-best-offer, product recommendation, or demand forecasting).

According to the PECB Lead Auditor Study Guide (Domain 1), ML is specifically referenced as the core technique for prediction systems, user behavior modeling, and data-driven decision-making systems.

Though Deep Learning (DL) is a subset of ML, it is often used for more complex pattern recognition tasks such as image or speech recognition, which is not explicitly required here.

It is the certification body’s responsibility to ensure that an audit plan is established prior to the audit.

ISO/IEC 17021-1:2015 Clause 9.2.3.1 requires certification bodies to “ensure that an audit plan is established, communicated, and agreed upon.”

Updating or communicating the plan can be tasks delegated to the audit team leader, but the accountability for establishing the audit plan remains with the certification body.

At least one member of the audit team must possess knowledge of the risk-based approach to auditing, particularly because ISO/IEC 42001 auditing requires risk-centric evaluation of AI processes.

ISO/IEC 17021-1:2015 Clause 9.2.1 emphasizes the importance of a risk-based audit approach.

The Lead Auditor Course for ISO/IEC 42001 states: “Competency in risk-based thinking is critical for identifying and focusing on AI system risks that could affect the achievement of audit objectives.”