Your question is Dense vs Keyword Embeddings. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Compare different embeddings strategies. When would you choose a dense vector search over a traditional keyword-based approach?
Implement a small retrieval experiment comparing a traditional TF-IDF or BM25-style keyword method with dense sentence embeddings. Explain the strengths and weaknesses of sparse word-level features, static embeddings, contextual embeddings, and sentence-transformer representations. Use preprocessing that preserves important search terms, evaluate both approaches with relevance judgments, and discuss hybrid retrieval, latency, index size, out-of-vocabulary terms, semantic matching, exact identifiers, and domain adaptation. Your answer should include runnable Python and a clear recommendation for when each strategy is appropriate.
