The engineering of Machine Learning (ML)-based systems is a complex task that involves integrating multiple disciplines and dealing with uncertainties inherent in ML techniques. To tackle this challenge, the confiance.ai research program ( https://www.confiance.ai/en/ ) has developed an end-to-end methodology for engineering ML-based systems. This methodology is based on existing standards and industrial practice and is supported by a web application called “the body-of-knowledge”. The end-to-end methodology provides guidelines covering all phases of the process of engineering ML-based systems. In this paper, we focus on the methodological framework as part of this methodology and that is dedicated to the operational analysis phase. This framework refers to a set of engineering activities captured in Capella models and guiding the operational analysis for Intended Purpose and Automation Objectives. This paper provides an overview of the proposed framework and illustrates its underlying steps with an example of an Automated Driving System (ADS).