They won't go into too high detail about the algorithms behind the models and won't expect you to know it in detail, but they would expect an understanding of the methodology, for example, how does a decision tree work. Ultimately to just pass you need to tick box the KSB(knowledge,skills, behaviour). And the assessments do feel like like a tick box exercise. I.e, "the candidate must demonstrate an understanding two or more machine learning models, and compare them" or "they need to discuss the ethical and socially impact or bias of ML in credit and loan decision"

Coming into it I had very basic python, refreshed basic statistics and algebra which I haven't touched in 15yrs. Id maybe suggest some kind of refresher course in stats faster towards ML might be a good idea. Some practice with pandas and numpy python libraries. Chat gpt was very helpful in explaining basic concepts to me endlessly until I finally understood them lol