Don’t know what your level of knowledge about Bayesian probability is but here is some stuff roughly ordered from easy to harder
For gaining some insight about different types of probability densities and mass functions:
Then for basic stuff on pymc here:
https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/
Beginner level model examples/modelling concepts (with pymc):
https://www.pymc.io/projects/examples/en/latest/blog/category/beginner.html
Cross-validation might be a topic that maybe comes up in an interview (don’t really have much interview experience, don’t take my word for it)?
If you have time before the interview and want to do some reading on Bayesian modelling then
could be a good start (also exists in the books section of core_botebooks link above). This notebook here contains exercises from this book solved in pymc:
https://nbviewer.org/github/cluhmann/DBDA-python/tree/master/Notebooks/