Uber Observability, SLOs & Reliability interview questions

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.

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.

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.

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.

Request-shaped SLIs quietly fail on data platforms. The strong answer defines freshness, completeness and deadline SLIs on consumer-visible artifacts, not job success rates.

MTTR is three clocks hiding in one number. Profile detection, diagnosis and fix separately from incident timestamps; attack whichever clock dominates, which is almost never rollback speed.

Their green page, your red SLO. Separate their true failure rate from the amplification your own client adds, buffer at the boundary, then escalate with a client-side scorecard instead of feelings.

Assert on the shape of a trace: which spans ran, in what order, with which attributes. Catches the cross-service regressions unit and contract tests structurally miss, at the price of instrumentation discipline.

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.

There is no status code and no caller waiting, so latency and error rate stop meaning what they meant. The signals that replace them are age, lag and completion, and they answer a question the request-response set never had to ask.

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

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.

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.

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.

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.

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.