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Reliability Observability and Incident Response Questions

Covers designing, building, and operating systems to be reliable, observable, and resilient, together with the operational practices for detecting, responding to, and learning from incidents. Instrumentation and observability topics include selecting and defining meaningful metrics and service level objectives and service level agreements, time series collection, dashboards, structured and contextual logs, distributed tracing, and sampling strategies. Monitoring and alerting topics cover setting effective alert thresholds to avoid alert fatigue, anomaly detection, alert routing and escalation, and designing signals that indicate degraded operation or regional failures. Reliability and fault tolerance topics include redundancy, replication, retries with idempotency, circuit breakers, bulkheads, graceful degradation, health checks, automatic failover, canary deployments, progressive rollbacks, capacity planning, disaster recovery and business continuity planning, backups, and data integrity practices such as validation and safe retry semantics. Operational and incident response practices include on call practices, runbooks and runbook automation, incident command and coordination, containment and mitigation steps, root cause analysis and blameless post mortems, tracking and implementing action items, chaos engineering and fault injection to validate resilience, and continuous improvement and cultural practices that support rapid recovery and learning. Candidates are expected to reason about trade offs between reliability, velocity, and cost and to describe architectural and operational patterns that enable rapid diagnosis, safe deployments, and operability at scale.

EasyTechnical
0 practiced
Design liveness and readiness health endpoints for a stateful data service used in ingestion (for example, a microservice that consumes Kafka and writes to a database). Describe what checks belong on liveness vs readiness endpoints, expected response schema, and how Kubernetes should use each endpoint to manage the pod lifecycle.
HardSystem Design
0 practiced
Design a safe automated failover orchestration for a stateful streaming cluster (e.g., Kafka plus connectors) across multiple regions. Address leader election, quorum requirements, avoiding split-brain, safe consumer rebalancing, watermark management, and ensuring that failover does not cause duplicate writes to sinks.
HardTechnical
0 practiced
As head of data reliability, propose a measurable plan to improve mean time to incident detection (MTTI) and mean time to recovery (MTTR) across the data platform over the next year. Include hiring, tooling investments, runbook and playbook quality, SLO changes, runbook automation, training, and OKRs or metrics you would track.
HardTechnical
0 practiced
Describe architecture and algorithmic choices to ensure data integrity during network partitions for a distributed write-heavy system. Discuss options such as CRDTs, quorum writes, transactional replication, and application-level conflict resolution, and explain trade-offs in consistency, latency, and complexity.
EasyTechnical
0 practiced
Explain SLI, SLO, and SLA and how they relate to data platform reliability. For a reporting ETL that must deliver fresh data for business reports, propose one SLI, one SLO (with numeric target), and an SLA-level consequence, and justify your choices including measurement method and window.

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