Learning from Incidents and Post Incident Review Questions
Responding to incidents with curiosity rather than blame. Asking 'why' questions to understand root causes, proposing systemic improvements, and sharing knowledge from incidents with the team. Showing humility and demonstrating growth from past mistakes.
MediumSystem Design
0 practiced
Design a scalable model-drift monitoring architecture for 500 production models that detects both data drift and concept drift. Describe telemetry collection, storage tiers, anomaly detection algorithms, alerting, dashboarding, sampling strategy, and cost-control mechanisms.
EasyTechnical
0 practiced
You receive an alert 'prediction latency > 300ms' affecting a subset of requests. Describe the specific logs, distributed traces, telemetry, and sampling strategy you would use to determine whether the root cause is model computation, I/O or serialization, input preprocessing, or infrastructure degradation.
HardTechnical
0 practiced
A deployed feature inadvertently included PII which resulted in regulatory exposure. Walk through an incident response focused on containment, remediation, legal and compliance notification, root-cause analysis, and concrete changes to prevent recurrence while preserving necessary forensic evidence.
MediumTechnical
0 practiced
Design a runbook for an ML inference-service outage that is readable and actionable by a junior on-call engineer. Include verification steps, mitigation options, rollback steps, communication templates, and a post-incident checklist to complete after service is restored.
HardTechnical
0 practiced
Design an algorithmic approach to automatically tune alert thresholds for model-performance metrics. Your solution should reduce false positives while maintaining recall for true incidents. Describe training data, the optimization objective, evaluation methodology, and a production rollout plan.
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