Explain core ML concepts and design choices

Q: Explain core ML concepts and design choices

This question evaluates mastery of core machine learning fundamentals including probabilistic loss functions (cross-entropy and KL divergence), regularization and uncertainty techniques (dropout and MC dropout), normalization choices in large language models, optimizer behaviors (SGD, momentum, Adam), and policy optimization stability (PPO).

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

Question

ML Fundamentals — Interview Questions

Instructions

Answer the following five ML fundamentals questions. Use precise definitions, equations, and concise justifications. If a derivation is requested, show the algebra clearly.

Questions

Cross-entropy vs. KL divergence

Define cross-entropy H(p, q), entropy H(p), and KL divergence KL(p || q).
Derive the relationship H(p, q) = H(p) + KL(p || q) and explain its implications for classification loss.

Dropout: training/testing distribution mismatch

Explain how dropout at training induces a distribution mismatch at test time if used naively.
Describe practical fixes, including inverted dropout scaling and Monte Carlo (MC) dropout.
State when you would prefer each approach in practice.

Normalization in large language models (LLMs)

Justify why LLMs typically use LayerNorm instead of BatchNorm.
Discuss implications for sequence length, micro-batching/distributed training, and training stability (e.g., pre-LN vs. post-LN).

Optimizers: SGD, SGD with momentum, Adam

Write the update rules for each (include bias-corrections for Adam).
Compare convergence behavior and generalization trade-offs.
Give concrete guidance on when to choose each.

PPO (Proximal Policy Optimization)

Explain why PPO introduces a KL-based constraint/penalty or clipping and how this stabilizes policy updates.
Include the clipped surrogate objective and the KL-penalized objective.
Discuss key hyperparameters, tuning strategies (e.g., target KL, clip fraction), and common failure modes.

Explain core ML concepts and design choices

ML Fundamentals — Interview Questions

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Explain core ML concepts and design choices

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ML Fundamentals — Interview Questions

Instructions

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