Your question is Design Data Pipeline or Serving. 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).
Design a data pipeline or serving architecture for a specific ML application.
Choose a realistic ML application and explain the end-to-end design. Cover requirements, scale assumptions, data ingestion and labeling, training and feature computation, online or batch inference, evaluation, monitoring, and failure recovery. Make tradeoffs explicit, including latency, cost, freshness, training-serving skew, and feature drift.