Yep! Here are a few, I customize the questions a bit based on seniority and background. 1. In a 1-d gaussian with mean=0 and variance=1, what is p(x=0)? 2. If

Yep! Here are a few, I customize the questions a bit based on seniority and background. 1. In a 1-d gaussian with mean=0 and variance=1, what is p(x=0)? 2. If I give you a csv file with a header, how would you return a tsv file with columns 6 and 2? Just high-level, not actual implementation. 3. If I have some dataset (I change it up a lot, a common one is subway ridership by station and date), how would you create a visualization to give some insight to a fairly non-technical audience, e.g. the distribution of ridership by station. 4. If I gave you a house dataset with a bunch of attributes (e.g. year built, number of sq feet, presence of pool, zip code), how would you go about building a machine learning model that predicts whether the house is being rented or is owned (assuming you are provided with tagged data)? The selling price? How many times it has been sold? 5. What's a technology that you're excited about (could be a computing tool, a machine learning algorithm in a library, etc)? Tell me some of the pros and cons of it and competing technologies. We also give out a 4-6 hour task as homework, and evaluate the code quality, tools used, answers provided, etc.