Technical Depth Verification Questions
Tests genuine mastery in one or two technical domains claimed by the candidate. Involves deep dives into real world problems the candidate has worked on, the tradeoffs they encountered, architecture and implementation choices, performance and scalability considerations, debugging and failure modes, and lessons learned. The goal is to verify that claimed expertise is substantive rather than superficial by asking follow up questions about specific decisions, alternatives considered, and measurable outcomes.
EasyTechnical
74 practiced
Differentiate counters, gauges, histograms, and summaries in monitoring systems (for example Prometheus). For each metric type give one concrete SRE use-case (e.g., request rate, in-flight requests, request latency distribution, unique-user counts). Explain tradeoffs in ingest cost, queryability (percentiles vs aggregates), and how high-cardinality impacts each type.
MediumSystem Design
80 practiced
Design a distributed rate limiter that enforces 100 requests/min per user across 50 stateless application nodes. Describe algorithms (token-bucket vs leaky-bucket), data placement (central store vs local caches), how to handle clock skew and partitions, and the tradeoffs between correctness and latency.
MediumTechnical
65 practiced
You need to verify that a StatefulSet on Kubernetes fails over correctly when the leader pod is killed. Describe how you would implement automated leader-failover tests including readiness/liveness probes, pod disruption budgets, anti-affinity rules, and steps to measure failover correctness and time-to-restore. Include safe teardown steps to avoid production impact.
MediumTechnical
72 practiced
You claim you reduced the p95 latency of a payment service by 40%. Walk me through a real-world deep dive: what observability data you collected, how you identified root cause, the specific code/config changes you implemented, how you measured impact, and any unintended side effects or tradeoffs you observed.
HardSystem Design
70 practiced
Design a global rate-limiter that enforces 1000 requests/min per user across 5 regions with eventual consistency and minimal overage. Describe algorithm choices (local buckets with reconciliation, central token service), derive the worst-case over-allowance as a function of sync interval and number of regions, and explain how you would monitor and bound user-facing overruns.
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