Explain core ML and DL fundamentals

Q: Explain core ML and DL fundamentals

This is a Machine Learning interview question from DRW for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

ML/DL Concept Questions (Take‑home)

Provide concise, correct answers to each prompt.

PCA

What do the eigenvectors of the covariance matrix represent?
How do they relate to principal components and explained variance?

Decision Trees

Define Gini impurity.
Show how to compute it for a node.
Explain how it is used to choose splits.

Reinforcement Learning

Write the Bellman optimality equation (for V* or Q*).
Explain its role in policy evaluation and improvement.

Regularization (Dropout)

What is dropout?
How does it behave at training vs. inference time?
Why does it act as a regularizer?

Training Stability

What is gradient clipping and when is it useful?
How do residual connections in ResNets help mitigate vanishing gradients?

Optimization Landscape

Why are deep learning objectives typically non‑convex?
What are the practical implications for optimization (e.g., local minima vs. saddle points)?

Transformers

Describe scaled dot‑product attention.
Explain why the dot products are scaled by 1/sqrt(d_k).

Explain core ML and DL fundamentals

ML/DL Concept Questions (Take‑home)

Solution (Locked)

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