Katkat
Back

Answer core ML fundamentals questions

PinterestMachine Learning Engineer · Online Assessment
HardMachine Learning EngineerOnline AssessmentMachine Learning

Last updated

This question evaluates a candidate's mastery of core Machine Learning fundamentals, including classification metrics (precision and recall) with confusion-matrix interpretation, gradient concepts for optimization, ensemble learning tradeoffs, and the mechanics of a neural network forward pass.

You are asked several short ML fundamentals questions:

  1. Define precision and recall for a binary classifier and explain how they relate to a confusion matrix.
  2. Given a confusion matrix (TP, FP, TN, FN), compute precision and recall and explain what types of errors increase/decrease each metric.
  3. Answer basic questions about gradients (e.g., what a gradient represents, and how it is used in gradient descent).
  4. Discuss common ensemble learning tradeoffs (e.g., bagging vs boosting, bias–variance, compute/latency).
  5. Do a simple neural network forward pass by hand for a small feed-forward network (matrix multiply + bias + activation) and produce the final output.