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What kind of machine learning architectures have you had exposure to?

What does each coefficient in a logistic regression model tell us about the impact on the predicted variable?

How did you handle a situation where you couldn't meet a deadline?

When it comes to your current role, what is your biggest achievement?

When Meta had solely 'Comment' and 'Like' functionalities, how might you design a test to assess the incorporation of additional emotive reactions such as 'care' or 'sad'? What's your strategy for formulating an A/B test in this case?

What's the algorithm for computing the dot product of sparse matrices?

Tell me about a time you were proved wrong

Can you explain what DMatrix is in XGBoost? How does it differ from other data structures used in gradient boosting?

How does acknowledging the assumption of error in linear regression contribute to creating accurate models, and what does it mean?

Decision trees and bias and variance - could you give us insight into how they relate? And in particular, which of those two is more tricky to manage: high bias or high variance, and what's the explanation behind that?