Explain ML statistics and model design concepts
Overview
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.
Amazon
Jul 29, 2025, 8:05 AM
Machine Learning Engineer
Technical Screen
ML System Design
5
0
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.
Solution
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