The engineering of Machine Learning (ML)-based systems is a com-plex task that involves integrating multiple disciplines and dealing with uncer-tainties inherent in ML techniques. To tackle this challenge, the confiance.ai re-search program1 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 pro-cess of engineering ML-based systems. In this paper, we focus on the methodo-logical framework as part of this methodology and that is dedicated to the oper-ational analysis phase. This framework refers to a set of engineering activities captured in Capella models and guiding the operational analysis for Intended Pur-pose and Automation Objectives. This paper provides an overview of the pro-posed framework and illustrates its underlying steps with an example of an Au-tomated Driving System (ADS).