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Browse 4 real-world technical and behavioral interview questions about Alerting. Review scenarios, edge cases, and architectural best practices.
A single threshold on a single window cannot separate a real outage from a brief spike, so it either pages on noise or misses slow burns. Replace it with multi-window multi-burn-rate alerting: a fast rule that pages and a slow rule that files a ticket, each gated by a short window so a spike that already stopped does not keep paging.
A green dashboard and a dead pipeline look identical, so absence of data has to be an alerting condition in its own right: heartbeats that fire when they stop arriving, an alerting path that does not share infrastructure with production, and at least one signal produced outside the system being measured. It also connects monitoring pipeline to the point an interviewer is testing.
An SLO defines the reliability users should experience, and the error budget is the allowed gap between that target and perfection. Use burn rate to decide when reliability work must outrank feature delivery.
Trace every request with prompt and model versions, token counts and cost, then add sampled groundedness grading, refusal and retry rates, validation failures and implicit user signals. Alert on countable rates, because an averaged quality score moves too little and too late to page anyone.