Comprehensive operational topic covering the responsibilities, processes, and practices involved in supporting production systems and managing incidents. Candidates should be able to describe on call scheduling models and burden distribution across teams, expected incident volume and typical severity levels, incident triage steps and severity assessment to prioritize and escalate appropriately, and criteria for involving security teams or external vendors. It includes monitoring and alerting strategy, alert thresholds and noise reduction, service level objectives and service level indicators, and tooling for incident management. Candidates should also be able to explain runbooks and playbooks for common incident types, hands on troubleshooting during live incidents, root cause analysis approaches, deployment and rollback practices, and measures to reduce mean time to detection and mean time to recovery. The topic also covers incident communication practices, escalation procedures, post incident activities such as blameless postmortems and follow up actions for continuous improvement, and considerations about allocation of time between maintenance and feature work to preserve production readiness.
HardTechnical
100 practiced
Design a quarterly incident simulation (game-day) program across the enterprise. Specify cadence, types of scenarios (network partition, large release rollback, data corruption), participant roles, metrics to measure readiness (MTTR reduction, action-item closure rate), and how to convert simulation learnings into process changes and tracked improvements.
HardTechnical
88 practiced
As an engineering lead, create a plan to allocate team time between maintenance (bug fixes, platform health, on-call participation) and new feature work for the next quarter. Include a quantitative method (e.g., X% of sprint capacity for maintenance), risk assessment tied to SLOs, gating mechanisms to shift priorities, and governance to ensure production readiness is maintained.
MediumSystem Design
81 practiced
How would you implement automatic rollback for failed Kubernetes deployments? Describe detection mechanisms (readiness/liveness probes, canary analysis), the tooling you would use (kubectl, ArgoCD, Flux, CI), rollback strategy, safety checks to avoid flapping, and observability to verify the rollback succeeded.
MediumTechnical
95 practiced
For a core authentication microservice, propose a complete SLO and error-budget policy: define one or two SLIs, set SLO targets and measurement windows, show how to compute the error budget, and outline automatic and manual actions (throttling, pause deploys, resource allocation) when the error budget is consumed.
MediumTechnical
98 practiced
Describe a concrete plan to measure and reduce Mean Time To Detection (MTTD) for a microservice over a quarter. Include instrumentation changes, alert routing improvements, synthetic checks vs real-user metrics, dashboards, and an experiment you would run to validate improvement.
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