How do you handle late-arriving data in a streaming or incremental pipeline?
Events arrive minutes or days after they happened, and a window that has already closed reports wrong counts. The signal is event-time vs processing-time, watermarks with allowed lateness, and how you fix already-emitted aggregates.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Events arrive minutes or days after they happened, and a window that has already closed reports wrong counts. The signal is event-time vs processing-time, watermarks with allowed lateness, and how you fix already-emitted aggregates.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
