Handle cold start, dropout, and training stability
Company: Amazon
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
## Machine Learning deep dive
Answer the following conceptual questions (you may use equations and small examples).
### A) Recommender systems: cold start
1. How do you handle **cold start** (new users and/or new items) in a recommendation system?
2. If a user is **brand new** with **no historical behavior**, what should your model output?
3. How do you handle **bias** introduced by missing history or exposure/selection effects?
### B) Dropout
1. Explain the difference between **training** and **inference** behavior for dropout.
2. Why is there a **scaling factor** (e.g., dividing by `1-p` or multiplying by `1/(1-p)`) during training in inverted dropout?
3. What happens if you **enable dropout at test time**?
### C) Optimization choices
1. What learning-rate scheduler did you use (e.g., step, cosine, plateau-based)? Why was it appropriate?
2. Explain **gradient clipping** and when it becomes necessary.
### D) Learning theory in practice
1. Explain the **bias–variance trade-off**.
2. Explain **double descent**: why can test error decrease again after model capacity/training passes a certain threshold?
Quick Answer: This question evaluates a candidate's understanding of recommender-system cold-start handling, dropout training versus inference behavior, optimization choices such as learning-rate scheduling and gradient clipping, and learning-theory topics like the bias–variance trade-off and double descent, emphasizing competencies in model regularization, exposure-bias mitigation, and training stability. It is commonly asked in the Machine Learning domain to assess both conceptual understanding and practical application for robust model training and generalization, combining theoretical reasoning with production-oriented considerations.
