Flipkart Observability, SLOs & Reliability interview questions

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.