Explain key ML theory and techniques

Q: Explain key ML theory and techniques

This question evaluates a candidate's understanding of core machine learning theory and practical modeling techniques, including parallelism in gradient-boosted trees (XGBoost), layer normalization in Transformer layers, multimodal neural network design and fusion strategies, collaborative filtering approaches, multi-armed bandit problem formulation and algorithms, and the probabilistic derivation and interpretation of logistic regression. It is commonly asked in technical interviews to assess breadth and depth across scalability, neural architecture and normalization choices, recommendation and online decision-making methods, and statistical modeling and regularization, and it falls within the Machine Learning domain testing both conceptual understanding and practical application.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

Onsite Machine Learning Engineer: Mixed Topics

You are asked to answer concisely but with depth across the following topics:

1) XGBoost Parallel Computation

Explain how XGBoost achieves parallelism during training. State what can and cannot be parallelized and why.

2) Transformer Layer Normalization

Explain layer normalization in a Transformer block, including where it is applied (pre-LN vs post-LN), the formula, and why it is used instead of batch normalization.

3) Multimodal Neural Network Architecture

Describe a general architecture for a multimodal neural network (e.g., text + image, or tabular + text). Include common fusion strategies and how to handle missing modalities.

4) Collaborative Filtering

Explain how collaborative filtering works, contrasting memory-based and model-based approaches. Provide the core formulas and how predictions are made.

5) Multi-Armed Bandit: Formulate and Solve

Formulate the K-armed bandit problem and present at least two solution algorithms (e.g., UCB, Thompson Sampling). Show a small numeric example and discuss regret.

6) Logistic Regression: Derive and Interpret

Derive logistic regression from a probabilistic viewpoint, provide the log-likelihood and gradient, and interpret coefficients. Mention regularization and decision boundaries.