Question 1

What is the difference between overfitting and underfitting?

Accepted Answer

Overfitting occurs when a model learns noise in training data and performs poorly on unseen data. Underfitting means the model is too simple to capture patterns. Address overfitting with regularization, cross-validation, and more data. Fix underfitting with a more complex model or better features.

Question 2

How do you handle class imbalance?

Accepted Answer

Common approaches include oversampling (SMOTE), undersampling, class weights in the loss function, choosing better metrics (F1, AUC-ROC instead of accuracy), and ensemble methods designed for imbalanced data.

Question 3

What are transformers and why are they important?

Accepted Answer

Transformers are neural architectures based on self-attention that process all tokens in parallel. They are the foundation of modern LLMs. Their ability to capture long-range dependencies and parallelize training made them far more effective than RNNs and LSTMs.

Question 4

What is RAG and when would you use it?

Accepted Answer

RAG (Retrieval-Augmented Generation) combines retrieval with generation. Instead of relying solely on LLM knowledge, RAG retrieves relevant documents from an external knowledge base as context. Use it when you need factual accuracy, domain-specific knowledge, or up-to-date information.

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