Google Observability, SLOs & Reliability interview questions

The arithmetic takes ten seconds and is the part most often skipped. The stall is the signal: it means error budgets are something you have read about rather than used.

Alert fatigue is a design failure, not a discipline failure. The fix is a rule about what may page at all, and burn-rate alerting is how you implement it.

Day-one telemetry is cheap and retrofitting it costs an incident. The answer that scores names the linking layer (trace IDs in logs, exemplars on metrics) that turns three tools into one story.

The roles question is really a delegation test: can you split commanding the incident from fixing it? Name the roles, the comms cadence, and the handoff discipline that keeps 3am coherent.

Spending the budget is arithmetic; enforcing consequences is politics plus automation. The answer that lands names trigger thresholds, the deploy gate that blocks releases, and the dated escape valve.

Facilitation is mechanics: timeline before theories, blame converted into system questions, actions with owners tracked to closure. Include the defensive engineer and the exec who wants a head.

Internal platforms hide a trap: the direct user is an engineer, but the real traffic belongs to their customers. Define SLIs from the consumer's vantage and turn the error budget into roadmap currency.

Monitoring dies silently: stuck alert queues, full TSDBs, collectors killed inside the same blast radius. Layer independent checks and a heartbeat so absence of signal pages louder than any red dashboard.

Tracing tells you which service and which span. It stops at the function boundary, and that is exactly where a CPU regression lives. Continuous profiling is the layer most teams do not have when they need it.

HTTP success can carry a rejected-span count. Replaying that batch can duplicate accepted telemetry, while enlarging the queue cannot repair invalid data.

Tracing is the only telemetry that answers where the time went across services. The mechanism is context propagation, and the reason teams fail at it is that one uninstrumented hop breaks the chain.

All three can search logs. What differs is who operates the cluster and how the bill is shaped, and the bill is what kills these projects, not the querying.

Where linear models break: compounding growth, step functions, peak days and saturation cliffs. Forecast per constrained resource, backtest, subtract lead time to get the real order-by date.

Prometheus asks your service for numbers on a schedule rather than waiting to be told. That one design choice explains service discovery, exporters, staleness and why short-lived jobs are the awkward case.

Metrics quantify impact, traces expose instrumented request paths, and logs describe individual events. Follow a slow checkout across all three while accounting for missing evidence.

Defining chaos engineering is easy. Far fewer can say what they would break first, and almost none volunteer the abort condition, which is the thing that makes it a programme rather than an incident.