Preparing for ML Breadth Interviews

ML interviews for FAANG+ companies typically consist of an ML breadth round. What is the most effective/efficient approach to prepare for these interviews?

For context, during my Amazon ML interview, I was asked questions ranging from "discuss how you would use random forests for regression (instead of classification)" to "why do ROPE embeddings enable larger context lengths in transformers".