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
An A/B experiment for a ranking model shows variant B reduces conversion by 4% with p<0.05. Outline an incident analysis plan to determine whether the drop is caused by the model change, sampling bias, instrumentation issues, or external factors. Include statistical checks you would run, segmentation and stratification approaches, pre/post checks, exploration of confounders, and explicit rollback criteria for the experiment.
MediumSystem Design
0 practiced
Provide a practical blameless postmortem template tailored for ML incidents. Include these sections and short example sentences: Summary, Severity, Impact, Timeline, Root Cause, Contributing Factors, Corrective Actions (with owner and due date), Verification Plan, Lessons Learned, and Follow-up Work. Explain how the template wording enforces blamelessness and aims to produce durable improvements.
HardTechnical
0 practiced
For ML systems in healthcare, discuss the trade-offs between automatic rollback (triggered by SLO violations) and manual human-in-the-loop gating. Cover regulatory reporting obligations, patient safety risk, speed of detection, risk of flapping rollbacks, auditing requirements, and propose a hybrid policy that minimizes patient risk while enabling timely mitigations.
HardTechnical
0 practiced
Explain how you would apply counterfactual analysis and causal graphs to test whether a recently deployed feature caused a production incident. Provide an example of an experiment design that yields causal evidence without fully reverting the feature (e.g., randomized holdout, targeted randomization, or using instrumental variables), and discuss practical constraints.
HardSystem Design
0 practiced
A fix requires changing a downstream API contract used by 20 internal services. Design a coordinated rollout and verification plan to minimize customer impact: include deprecation timelines, backward-compatible change patterns, automated compatibility tests, consumer migration tracking, and a post-incident audit to ensure no undocumented dependencies remain.
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