Your question is Database Architecture 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).
Can you explain the trade-offs between different database architectures for a high-volume application?
Discuss how you would compare relational, distributed key-value, wide-column, document, analytical, and vector databases for an end-to-end ML system. Cover workload fit, consistency, partitioning, scaling, latency, cost, operational complexity, and failure recovery, including how database choices affect retrieval, ranking features, training data, and monitoring.