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Built by a hiring manager who's conducted 1,000+ interviews at Google, Amazon, Nvidia, and Adobe.
Last updated: December 9, 2025
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Site Reliability Engineering interviews assess your ability to build and maintain highly reliable, scalable distributed systems through software engineering and operational excellence. Expect questions covering SLIs/SLOs/SLAs, monitoring and observability, incident response, automation, capacity planning, and system design for reliability. Success requires demonstrating both strong software engineering skills and deep operational expertise in managing production systems at scale.
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SLI (Service Level Indicator) is quantitative measure of service aspect (request latency, error rate, throughput). SLO (Service Level Objective) is target range for SLI (99.9% requests succeed, p95 latency under 200ms). SLA (Service Level Agreement) is business contract with consequences if SLO not met. For web service, SLIs might include availability (successful requests/total), latency (time to first byte), and error rate. SLOs set targets (99.9% availability, 95% requests under 200ms). SLAs include financial penalties for missing SLOs.
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Error budget is allowed unreliability (1 - SLO) that can be spent on rapid feature releases, risky changes, or expected failures. If SLO is 99.9% availability, error budget is 0.1% downtime. While within budget, teams can move fast. When budget exhausted, focus shifts to reliability improvements, slow down releases. Prevents over-investment in reliability (diminishing returns) and under-investment (customer impact). Measured continuously, reviewed regularly. Discuss how it creates data-driven decisions about reliability vs velocity trade-offs.
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Implement multi-layer monitoring - application metrics (RED pattern for rate, errors, duration), infrastructure metrics (USE pattern for utilization, saturation, errors), business metrics. Use service mesh or instrumentation for distributed tracing. Set up metrics collection (Prometheus), visualization (Grafana), log aggregation (ELK), and distributed tracing (Jaeger). Create actionable alerts based on SLO violations, not symptoms. Use multi-window multi-burn-rate alerting to reduce noise. Implement runbooks for common issues. Include health checks, synthetic monitoring, and anomaly detection. Discuss avoiding alert fatigue.
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Immediate response - acknowledge alert, assess severity, declare incident if needed, page on-call team. Triage - identify scope, check recent changes, review monitoring dashboards, check dependencies. Communicate - update status page, notify stakeholders, establish incident commander if major. Mitigate - roll back changes, failover to backup, scale resources, implement workarounds. Resolve - fix root cause, verify resolution, clear alerts. Post-incident - conduct blameless post-mortem, identify action items, update runbooks, share learnings. Emphasize clear communication and documentation throughout.
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Use Google's Four Golden Signals - Latency (response time distribution, p50/p95/p99), Traffic (requests per second), Errors (error rate by type, 4xx vs 5xx), Saturation (resource utilization including CPU, memory, disk, network). Add application-specific metrics like active users, transaction success rate, queue depth. Infrastructure metrics including database connection pool, cache hit rate. Measure from both server-side and client-side (real user monitoring). Set up dashboards showing trends and enable correlation during incidents. Discuss choosing metrics that matter to users and business.
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Design with no single points of failure: multi-region deployment, load balancing with health checks, database replication with automatic failover, redundant components across availability zones. Implement graceful degradation and circuit breakers for dependencies. Use canary deployments and blue-green deployments for safe releases. Design for failure: retry logic with exponential backoff, timeouts, bulkheads. Monitor everything and automate recovery. Calculate allowed downtime (52.6 min/year) and plan maintenance windows accordingly. Test failure scenarios regularly with chaos engineering. Discuss trade-offs with cost and complexity.
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Toil is manual, repetitive, automatable work with no enduring value that scales linearly with service growth (paging, manual deployments, ticket queue). SRE goal is keeping toil <50% of time to allow engineering work (automation, tooling, reliability improvements). Reducing toil through automation improves: team satisfaction, service reliability (humans make mistakes), scalability (engineers handle more services), and engineering culture. Identify toil by asking: is it manual, repetitive, automatable, tactical, no enduring value, scales with service? Prioritize automation projects by impact and effort.
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Capacity planning ensures sufficient resources to meet demand with acceptable performance. Process - establish current baselines (utilization, throughput, latency), model growth based on business projections and historical trends, identify bottlenecks through load testing, project resource needs with safety margin (typically 30-50%), plan procurement timelines. For rapid growth - implement auto-scaling for elastic capacity, use leading indicators (signups, engagement), overprovision initially, review quarterly with adjusted forecasts. Monitor actual vs predicted usage. Test at projected scale. Discuss organic vs launch-driven growth and multi-region considerations.
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Focus on systems and processes, not individuals. Structure - timeline of events, root cause analysis (5 whys, fault tree), impact assessment, what went well, what went poorly, action items with owners and deadlines. Create psychologically safe environment emphasizing learning over blame. Involve all participants. Document thoroughly and share widely. Action items should address systemic issues, not just symptoms. Follow up on action items in future incidents. Discuss learning from incidents as opportunity for improvement. Mention post-mortems for near-misses too. Avoid 'human error' as root cause - dig deeper into systemic failures.
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Use STAR method describing specific reliability problem (frequent outages, cascading failures, slow performance). Explain analysis approach using data (monitoring, logs, post-mortems), identifying root causes and systemic issues. Describe solution implemented (architectural changes, monitoring improvements, automation, process changes). Quantify improvements with metrics (reduced MTTR, improved availability, decreased incidents). Discuss challenges faced and trade-offs made. Emphasize systematic approach, collaboration with teams, and measuring impact. Share lessons learned and how you applied them to other systems.
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Monitoring checks known failure modes with predefined metrics and alerts - asking known questions. Observability enables understanding system behavior and debugging unknown issues - answering unknown questions. Monitoring uses metrics dashboards and threshold alerts. Observability uses metrics, logs, and traces together to explore and understand system state dynamically. Observability crucial for complex distributed systems where failure modes unpredictable. Achieve through high-cardinality data, rich context, correlation across signals. Both important: monitoring for known issues, observability for novel problems. Discuss tools supporting each approach.
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Implement multi-stage rollout - canary deployment (1-5% traffic), staging environment testing, gradual rollout with monitoring at each stage. Define rollback triggers and criteria (error rate increase, latency degradation, SLO violations). Use feature flags for quick disable without deployment. Implement automatic rollback on metric degradation. Monitor key metrics throughout - error rates, latency, resource usage, business metrics. Use traffic splitting at load balancer or service mesh. Test rollback procedures regularly. Start with less critical services or regions. Discuss A/B testing for validating changes and percentage-based rollouts with automated progression.
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Circuit breaker prevents cascading failures by stopping requests to failing dependency. States - Closed (normal, requests pass through), Open (dependency failing, requests fail fast), Half-Open (testing if dependency recovered). Use when calling external services, protecting against dependency failures, preventing resource exhaustion from retries. Benefits - fail fast instead of waiting for timeouts, give failing service time to recover, preserve resources. Configure thresholds (failure rate, consecutive failures), timeout for open state, success threshold for half-open to closed transition. Combine with retry logic, timeouts, and bulkhead patterns.
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Use error budgets as quantitative framework for decision-making. While within budget, prioritize feature velocity. When budget exhausted, prioritize reliability work. Invest in automation and tooling to improve both reliability and velocity. Implement safe deployment practices (progressive rollouts, feature flags) enabling fast, reliable releases. Build reliability into development process (testing, code review, monitoring) rather than after the fact. Use SLOs to avoid over-engineering reliability (99.9% vs 99.999% has different costs). Discuss with stakeholders in terms of business impact, not just technical metrics. Culture shift: reliability enables velocity.
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Chaos engineering proactively tests system resilience by injecting controlled failures to identify weaknesses before they cause outages. Start small - non-production environments, during business hours, small blast radius, with abort mechanisms. Common experiments - terminate instances, introduce latency, fail dependencies, network partitions, resource exhaustion. Tools include Chaos Monkey, Gremlin, Litmus. Process - define steady state (SLO metrics), hypothesize what happens during failure, run experiment with minimal blast radius, monitor and learn, expand scope gradually. Requirements include good monitoring, automatic recovery mechanisms, stakeholder buy-in, runbooks. GameDays for larger tests.
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