And you learn literally zero about a candidate's ability to understand when and why things work by asking questions about eigenvectors. Someone can understand what an eigenvector is and still not have any clue about how you figure out a system is working, why it's working, what is likely to happen in production, how you test the limits of your method's ability to generalize, how you take an real problem and find something that you can productively use ML on, etc.

People say things like "you need to know how it works" but "it" doesn't work using your knowledge of eigenvectors. If you want to test how "it" works, test that, literally. Put up a model on the board and a dataset. Ask people about what might happen when you apply one to the other. What changes they would make in response to changes in the data. What they would do in response to the following training curves, budget limitations, etc.

These interviews are terrible and they select for people that regurgitate facts.