Dynamic mode decomposition (DMD) is a data-driven technique widely used to analyze and model fluid problems, including transonic buffet flows. Despite its strengths, DMD is known to suffer from sensitivities to the selected settings and the characteristics of the data used. In this work, we closely examine the aforementioned sensitivities, identify possible pitfalls, and provide best practices for robustly performing DMD on a flow exhibiting transonic shock buffet. Specifically, we assess several DMD variants and test their sensitivity to the POD rank truncation and the sampling rate. A critical enabler to our analysis is a new presentation of the DMD algorithm as a modular framework consisting of five distinct steps. The tests also highlight the existing dangers of aliasing, when the sampling rate is too low. Finally, a list of practical recommendations and guidelines on how to accurately and robustly perform DMD on a transonic buffet flow is provided.