Databricks ML Interview: Neural Networks & Transformers

This question focuses on neural network architectures used in NLP, with emphasis on the Transformer family and classic Word2Vec embeddings. You will be expected to explain core Transformer components — encoder/decoder blocks, multi-head self-attention, feed-forward layers, and positional encoding — and to contrast their roles in sequence modeling and attention-based processing.

You should also demonstrate understanding of Word2Vec training paradigms (Skip-gram vs CBOW), how input/output pairs differ, and common training optimizations (negative sampling, hierarchical softmax). Expect to walk through both conceptual diagrams and concrete computations: e.g., derive scaled dot-product attention for a small toy example, show how positional encodings are added, or explain how embedding vectors capture semantic similarity.

Typical interview flow: initial conceptual questions to probe fundamentals, a whiteboard or diagram stage to design or modify a Transformer block, and follow-ups that dive into implementation details, complexity/trade-offs, or evaluation methods. You may be asked about scaling Transformers to long contexts (sparse or memory-compressed attention), fine-tuning strategies, and how to evaluate embeddings (intrinsic vs extrinsic metrics).

Skill signals you should demonstrate: knowledge of attention mechanisms, hands-on familiarity with PyTorch/TensorFlow APIs, solid linear algebra and optimization intuition, and practical NLP considerations such as tokenization, embedding dimensionality choices, and downstream evaluation.