Cloudflare Observability, SLOs & Reliability interview questions

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.

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.

The observability failure that scales with your success. Cardinality is the cost driver in every metrics system, and the fix has to be structural or it comes back next quarter.

The instinct is to delete old logs. That is backwards: the waste lives in what you ingest, not how long you keep it. This answer walks the levers in the order that actually pays.

The pull model is the famous part and explains none of the operational pain. The storage engine is where Prometheus hurts in production: crash recovery, cold queries, disk death.

Dashboard sprawl is an operating-model problem wearing a tooling costume. The scored answer moves definitions into git, attaches ownership metadata to every panel, and deletes without ceremony.

The kernel watches syscalls, sockets and short-lived processes that userspace agents polling every few seconds never meet. Know what eBPF adds, what it costs, and the security conversation it starts.

Forty healthy islands equal one blind estate. Compare the consolidation patterns honestly, keep alerting local, curate what crosses the WAN, and make the global view read-only and cheap.

Rotation length decides focus versus fatigue, handoff quality decides whether Tuesday inherits Monday's mess, and follow-the-sun only works when every region holds real authority.

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.

You have added a dependency with a latency distribution an order of magnitude wider than anything else in your stack, whose failure mode is a plausible wrong answer rather than an error.

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.

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.

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.

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.

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.