Post Incident Analysis and Improvement Questions

Covers the end to end process of investigating incidents and converting findings into durable program improvements. Candidates should be able to describe how to run structured post incident reviews and root cause analyses that probe beyond the immediate failure to uncover underlying system, process, human, and governance causes. Topics include evidence collection, timeline reconstruction, causal analysis techniques, identification and prioritization of corrective actions, remediation tracking and verification, validating effectiveness of fixes, communicating lessons learned across teams, and using incident data to inform risk assessments and policy or process changes. Emphasis should be placed on practical examples of preventing recurrence, balancing near term containment with long term fixes, and building a blameless culture that supports continuous improvement.

MediumTechnical

0 practiced

Explain how tools like MLflow or Weights & Biases, together with dataset versioning (e.g., DVC) and containerized pipelines, help forensic investigations after an ML incident. Provide a step-by-step example of reproducing a failing inference when you are given only an inference_id and a timestamp (which artifacts you pull and commands you run).

MediumTechnical

0 practiced

A monitoring pipeline was inadvertently disabled by a CI/CD change and remained down for 36 hours. Explain the forensic steps to identify the exact commit or config that caused the outage, how to roll back and restore monitoring, and what CI/CD safeguards (tests, approvals, deploy windows, monitoring-for-monitoring) you would add to prevent recurrence.

MediumTechnical

0 practiced

A subtle bug is affecting 0.5% of users who have very high lifetime value. Describe instrumentation and detection strategies to catch such cohort-specific failures: what feature tags, user identifiers, logging granularity, cohort metrics, and alerting approaches would you add, and how would you perform statistical tests to confirm cohort impact?

MediumTechnical

0 practiced

You're on-call and notice inference latency for an online recommendation model increased 3x immediately after a model update. Describe step-by-step the evidence collection and timeline reconstruction you would perform: include which Kubernetes commands you'd run (e.g., kubectl describe events, kubectl top), Prometheus queries for latency and resource metrics, how you'd fetch recent inference logs, feature-store health checks, and how you'd compare recent experiment runs to previous ones to isolate the cause (model vs infra vs data).

EasyBehavioral

0 practiced

Describe what a true 'blameless' post-incident culture looks like in an ML organization. Use the STAR method to tell a short story about a real or hypothetical review where the team identified systemic process changes instead of blaming an individual, and explain how that review led to measurable improvements.

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