Meta Data Science interview questions

SQL: How to join tables with common fields, how to calculate sums and averages, etc. Quite basic, nothing advanced such as window functions or DDL. Data Science math: Calculate expected values, calculate probabilities of simultaneous occurrence of independent events, etc. Pretty basic, nothing advanced like weird distributions or hard to remember facts. Data Science Analysis: Come up with plausible metrics to evaluate a recommendation system. How would you test the recommendation system for bias? What hypotheses can you come up with for detecting bias, and how would you test them? How do you test whether a new system is a net positive or it is cannibalizing an existing system. Behavioral: Tell me about some work you are proud of. How would you handle ambiguity - how do you make recommendations in the face of ambiguity? What do you like about work, what do you dislike?