Datadog Observability, SLOs & Reliability interview questions

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

Every candidate can define chaos engineering. 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.

A question with a right answer and a subtle reason. Histogram versus summary is the part that catches people, and it turns on whether you can aggregate across instances.

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.

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.

OpenTelemetry is an API and a wire protocol, not a product. Candidates who promise instant multi-vendor bliss have never run the collector in production or negotiated with a vendor who knows it.

Most candidates recite the pull model and stop. The interviewer asking this wants the storage engine story, because that is where Prometheus hurts in production: crash recovery, cold queries, disk death.

Choose what evidence must survive, then price the collection path. A tail policy can only inspect traces that reach it; low SDK sampling changes the guarantee before the collector sees a span.

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.

Routing rots because orgs change faster than configs. The senior answer treats ownership as data resolved from a catalog at fire time, with drift detection on the routing itself.

Attribution is an incentive-design problem: own-team labels at ingest, unit pricing per signal, monthly showback before any chargeback, sanctioned levers so teams can actually respond.

Detectors earn their keep on seasonal aggregates and fleets of similar series, run in shadow mode with measured precision. They lie on low-volume series, bursty workloads and regime changes.

One surface cannot serve the responder and the review. Tier dashboards by the question each audience asks, and make every tile answer in five seconds or link one click deeper.

Sampling keeps proportions and loses rare individuals; filtering keeps chosen classes complete and loses whatever you misclassified. Decide retention at write time, keep counters always, rehydrate the rest.

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.

Agent per node, gateway per cluster, and one decision that shapes everything else: tail sampling needs every span of a trace in one place, which is why the topology has a tier that looks redundant until you need it.

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.

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.

Anyone can divide bad requests by total. This answer shows the aggregation mistakes that quietly corrupt SLO dashboards, the rate-versus-increase traps that make budgets lie, and when subqueries cost too much.

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 data-path question separates people who have sized a cluster from people who installed one. Shard sizing, refresh versus flush, and each ILM phase carry one specific failure mode apiece.

Metrics describe the server's experience; synthetics describe the user's. The strong answer designs a few money-path journeys from real geographies, pages only on persistent failure, and budgets for script rot.