Gaming the interview process

People often ask me: “Don’t you worry that candidates will just memorize the answers in this book and game the system?”

First, I don’t encourage interviewers to ask the exact questions in this book, but I hope this book provides a framework for interviewers to distinguish good questions from bad ones.

Second, there’s nothing wrong with memorizing something as long as that memorization is useful. The problem begins when memorization is impractical -- candidates memorize something to pass the interviews and never use that knowledge again, or don’t know how to use it in real situations.

For this book, I aimed to include only concepts that I and many of my helpful colleagues deemed practical. For every concept, I ask: “Where in the real world is it used?” If I can’t find a good answer after extensive research, the concept is discarded. For example, while I chose to include questions about inner product and outer product, I left out cross product. You can see the list of discarded questions in the list of “Bad questions” on the book's GitHub repository. This is far from a foolproof process. As the field expands, concepts that aren’t applicable now might be all that AI researchers ever talk about in 2030.

Interviews are stressful, even more so when they are for your dream job. As someone who has been in your shoes, and might again be in your shoes in the future, I just want to tell you that it doesn’t have to be so bad. Each interview is a learning experience. An offer is great, but a rejection isn’t necessarily a bad thing and is never the end of the world.

There are many random variables that influence the outcome of an interview: the questions asked, other candidates the interviewer has seen before you, after you, the interviewer’s expectation, even the interviewer’s mood. It is, in no way, a reflection of your ability or your self-worth.

I was pretty much rejected for every job I applied to when I began this process. Now I just get rejected at a less frequent rate. Keep on learning and improving. You’ve got this!

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