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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.

MediumSystem Design
0 practiced
Design SLOs for data freshness and completeness for a near-real-time reporting pipeline that serves dashboards to executives. Recommend SLIs, numeric SLO targets, measurement windows, alert thresholds, and a roll-back or mitigation strategy when SLOs are breached. Explain how you would measure and attribute SLO violations to pipeline components.
HardTechnical
0 practiced
Design an experiment using canary SLOs to test whether a new ingest change impacts downstream dashboards. Specify instrumentation required on canary and control cohorts, metrics to compare, statistical confidence criteria, duration, rollback criteria, and how to automate decision-making based on SLO comparisons.
HardTechnical
0 practiced
Describe an architecture and concrete per-connector strategies to provide safe retry semantics across a streaming pipeline: for Kafka producers/consumers, database writes, REST calls, and object storage like S3. Explain how to achieve at-least-once and exactly-once guarantees where possible, and describe patterns like outbox, idempotent writes, and transactions.
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.
HardTechnical
0 practiced
Design an observability approach for ML pipelines: suggest SLIs for data drift, concept drift, model latency, prediction distribution, and explainability signals. Describe tooling and alerting approaches to detect model degradation and an automated rollback or mitigation strategy for problematic models.

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