Behavioral questions were heavily oriented towards the Amazon leadership qualities. > Name a time you were innovative > Name a time you delivered a simple solution to a complex problem. Follow up questions included how to quantify the level of success in projects brought up. Machine learning fundamentals: > How to deal with a troublesome dataset (interpretation open ended so think data cleaning, etc.) > How to deal with misrepresentative training data (imbalanced dataset, overfitting, explain how L1/L2 regularization work at an optimization level) > How to deal with a large dataset where only a few examples are labeled (semi-supervised learning) Coding question was: https://leetcode.com/problems/find-original-array-from-doubled-array/
Machine Learning Engineer Interview Questions
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HackerRank assessment included a variation of Leetcode's Shortest Path to Get Food problem
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Describe normalization and bayes rule
Q. Solve the given coding problems.
What kind of role is this
Q: Implement a function to check if a binary tree is balanced. Q: How would you evaluate the performance of a recommendation system, like the ones used on Amazon?
Q: Tell me about your background for this job Q: Explain how bagging and boosting models work
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