Your question is Embeddings and Vector Search Trade-offs. 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).
Explain the trade-offs between different embeddings and vector search indexing strategies for high-latency environments.
Discuss sparse and dense representations, embedding dimensionality, semantic quality, memory use, and query latency. Provide a practical Python experiment comparing at least two embedding approaches and two FAISS index types, including recall and latency evaluation.