Your question is Dashboard for Large Datasets and Live Updates. 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 dashboard for a cloud-based service that handles large datasets and real-time updates.
Clarify the dashboard users, data sources, freshness expectations, and whether ML should support anomaly detection, forecasting, search, or prioritization. Describe the end-to-end architecture, including ingestion, storage, feature computation, retrieval, ranking or insight prioritization, online updates, batch processing, and frontend delivery. Address scalability, latency, consistency, model evaluation, monitoring, feature drift, training-serving skew, and graceful degradation.
