Explain ML statistics and model design concepts

Q: Explain ML statistics and model design concepts

This question evaluates mastery of probability and statistics, machine-learning foundations, deep-learning architectures, causal inference, and end-to-end model design concepts within the ML System Design domain.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Technical Screen rounds at Amazon.

Q: What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Amazon during technical interviews.

Question

Technical Phone Screen: Theory + System Design

Probability and Statistics

Define a moment generating function (MGF) and explain how it is used.
Compare the exponential and Poisson distributions and explain how they relate.
Explain "statistical distance" vs. "statistical difference" (significance). Provide examples.
Describe the steps of a hypothesis test.
State and explain the Law of Large Numbers.

Machine Learning Foundations

What common assumptions underlie machine-learning models?
How does multicollinearity affect Random Forests versus XGBoost?
How does the number of trees impact performance (Random Forest vs. Gradient Boosting/XGBoost)?
Define overfitting and methods to prevent it.

Deep Learning Concepts

Name deep-learning model families you are familiar with.
Compare RNN and LSTM; explain RNN drawbacks and LSTM structure.
What is an autoencoder and what are typical use cases?
How does the attention mechanism work?
Describe the architecture of a CNN.

Causal Inference

Explain the basic principles of causal inference and common methods.

System Design

Design an ML system to identify relevant users (e.g., for targeting a new feature or campaign). Outline problem framing, data, model, serving, and evaluation.