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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.

MediumTechnical
47 practiced
Discuss trace sampling strategies for a high-throughput microservices data platform producing millions of traces per hour. Compare head-based sampling, tail-based sampling, adaptive sampling, and event-driven sampling. For each approach, explain implications for cost, debugging rare errors, and how you would preserve traces for error cases.
EasyTechnical
54 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.
HardTechnical
60 practiced
As a data engineering lead, propose an organization-wide SLO policy that covers streaming ingestion, batch ETL, and analytics query latency. Define SLO tiers, error budgets, enforcement mechanisms, developer workflows when budgets are exhausted, and incentives to balance velocity and reliability.
HardTechnical
60 practiced
Design observability strategies for serverless ETL functions (e.g., AWS Lambda or GCP Cloud Functions) that are ephemeral and scale rapidly. Cover structured logging, cold-start metrics, tracing across async steps, sampling, cost considerations, and how to correlate serverless invocations with downstream batch jobs and storage writes.
MediumTechnical
55 practiced
Create an alert routing and escalation policy for a multi-team data platform that includes ingestion, transformation, and serving teams. Specify which alerts go to which team, escalation timing, on-call rotations, playbook links, and how to avoid noisy cross-team pages while ensuring timely resolution for customer-impacting incidents.

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