{"blocks": [{"key": "aeaca8fc", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "eb0864f5", "text": "Design a real-time analytics pipeline that ingests website click events with Kafka, processes them in Flink, and writes queryable aggregates to a warehouse.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c3464fb5", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "e6ec6853", "text": "Describe the end-to-end pipeline: topic partitioning, Flink windowing, state management, and how you would model tables for downstream consumption. What failure-handling and back-pressure strategies would you employ?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "b6cd0c9e", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "56c21a50", "text": "Cover at-least-once semantics, checkpointing, exactly-once sinks, and schema evolution.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}