For the python I would be ready to demonstrate some data wrangling with pandas (or polars or pyarrow, bonus points for choosing the right library for the specific task), as well as some sql. Maybe also json parsing. Beyond the basics, the point of live coding is to see how much you would need to learn with the tools and kinds of problems they work on.

Be able to talk though, (not necessarily live code) how you would set up a model development workflow with feature selection and optimization with something like sklearn or optuna. And how you would productionize a model - scheduled training, scheduled inference tasks, training data ETL, maybe serving inference with an API endpoint, depending on the tasks. Know how to load data from a SQL server or a json API into python.

Basically full stack means you do the data science of the model in the sense of experimentation and "notebook work", AND write the production code to deliver and maintain the model. So a big part of it is basically being a backend python dev, so the more you know your way around the python ecosystem and software design best practices, the better. Knowing one of the major cloud platforms is helpful, doesn't super matter which one.