Advanced interview questions in Monitoring & SRE, page 2
Fixing an unmanaged post-mortem action backlog
Generate fewer, higher-leverage items targeting systemic risk; assign owners and dates; integrate into normal planning with explicit prioritization.
Measuring post-incident review effectiveness
Track action-item completion and age, repeat-incident rate, time-to-publish, and MTTR trend.
Reviewing a large-scale cascading outage
Dedicated facilitator, cross-team timeline reconciliation, map cascade chains and multiple contributing factors, layered action items.
Design a centralized auto-remediation platform
Event ingestion, a rules engine mapping alerts to playbooks, a sandboxed execution runtime, and guardrails like dry-run, rate limits, and rollback.
Measure ROI of toil reduction efforts
Track toil hours, percent of time on toil, incidents auto-resolved, and engineer cost saved, then frame as ROI and risk reduction.
Auto-rollback on failed blue-green cutover
Shift traffic gradually behind a smart router, use deep health checks plus real SLI monitoring, and auto-revert to blue on breach while blue stays warm.
Canary a shared downstream microservice
Route a slice of traffic to the canary via mesh rules, propagate context, and use distributed tracing to measure impact on upstream callers across the full path.
Why tail latency (p99) matters
Averages hide the worst experiences, and fan-out amplifies tails so most requests touch a slow path; causes include GC pauses, queueing, contention, and noisy neighbors.
When horizontal scaling is the wrong fix
Adding instances fails when the bottleneck is a shared resource like one database, a lock, or a queue, so more instances just add contention; investigate where time is actually spent.
Replication and consistency for active-active regions?
Choose per data class between synchronous (low RPO, higher latency) and async replication, address write conflicts, and reason via CAP and PACELC.
Design a chaos experiment for a payment dependency?
Define a measurable steady state, hypothesize it holds when payments fail, limit blast radius to a small traffic slice, and auto-abort on SLO breach.
Reliability paved roads for an internal PaaS?
Built-in observability, safe deploys with health checks and rollback, sane timeouts/retries/limits, and SLO tooling.
Chaos test for gray-failure cascades in shared services?
Inject partial latency into a shared service, hypothesize tenants stay isolated within steady state, and monitor cross-system queue depth, pool saturation, retries, and per-tenant SLIs.
Client-side chaos for an uncontrollable third party?
Inject faults at your client boundary via a proxy or fault-injecting wrapper, simulate timeouts, errors, and latency, then verify timeouts, retries, breakers, and fallbacks.
Designing an error budget policy
Define SLO and budget, tiered consequences as burn worsens, a feature freeze on exhaustion, and concrete earn-back criteria.
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