Alerting Strategy and Incident Response Questions

Design alerting strategies and incident response practices that turn observability signals into actionable operations. Topics include alert design and classification, threshold versus anomaly detection, preventing alert fatigue, escalation and on call flow, runbook and playbook design, integrating alerts with incident management, post incident review and blameless postmortems, and how monitoring and observability feed incident detection and mean time to resolution improvements. Includes designing alerts for different domains and thinking through what runbooks and context to provide to responders.

HardTechnical

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Propose a statistically rigorous method to detect concept drift in mainly unlabeled production traffic, where labels arrive later and sparsely. Include techniques such as two-sample tests, density-ratio estimation, bootstrapping for confidence, and how alerts should be throttled to avoid false positives.

EasyTechnical

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Explain the difference between data drift and concept drift in production ML systems. Give one concrete detection method for each and an example where labels are delayed by days how you'd still detect issues.

HardSystem Design

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Design a centralized alerting architecture capable of handling 10k ML models across multi-region deployments. Describe components (metrics ingestion, real-time detectors, batch drift analysis, storage, routing), data flow, latency guarantees for critical alerts, and mechanisms to prevent global alert storms.

MediumTechnical

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Design a runbook for this incident: 'A nightly batch feature pipeline wrote null values for a key feature for a single customer segment, causing degraded model performance for that segment.' Include triage checks, short-term mitigation, reprocessing/backfill logic, and long-term preventative actions.

HardTechnical

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A coordinated multi-region degradation occurs after a third-party data provider changed their schema. As incident commander, design the playbook to contain the incident: cross-team coordination (platform, ML teams, vendor), rollback/caching strategies, vendor communication, and legal/contractual next steps.

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