Problem Solving Leadership Questions
Leading the identification, analysis, and resolution of project issues and blockers at an organizational or cross functional level. Emphasis on diagnostic techniques to find root causes, setting clear escalation criteria, engaging and aligning stakeholders, facilitating collaborative decision making, implementing solutions, measuring effectiveness, and documenting postmortems and lessons learned. Candidates should demonstrate how they prioritize issues, communicate trade offs, drive consensus, and institutionalize improvements to prevent recurrence.
HardSystem Design
0 practiced
Design a resilient model serving architecture deployed active-active across two regions. Requirements: consistent model versions across regions, replicated feature store with low read latency, failover within 30 seconds, and guarantee no loss of model-inferred data used for auditing. Describe components, replication strategy, consistency model, and recovery procedures.
MediumTechnical
0 practiced
Describe a decision framework you would use to balance shipping ML improvements quickly against the risk of causing incidents. Include how you score risk, mandatory mitigations for higher-risk releases (tests, canaries), approval gates, and who should sign off for different risk levels.
EasyTechnical
0 practiced
Explain SLIs (Service Level Indicators) and SLOs (Service Level Objectives) specifically for production ML systems. Provide three concrete SLI examples for a recommendation model (e.g., latency, relevance, business metric), explain how to set SLO thresholds, and describe how error budgets should influence remediation priorities.
MediumTechnical
0 practiced
Implement a Python function detect_drift(train_values, prod_values, alpha=0.05) that uses the Kolmogorov-Smirnov two-sample test to detect distribution drift between training and production numeric feature arrays. The function should return a dict {'p_value': float, 'drift': bool}. Assume numpy and scipy are available; include a short docstring and an example call.
MediumSystem Design
0 practiced
Design an incident runbook for a real-time inventory-forecasting ML model that affects order fulfillment. The runbook should include: detection triggers, triage checklist, roles and responsibilities, rollback criteria, communication templates for ops and customers, and recovery validation metrics. Assume 1,000 requests/sec and 1,000 SKUs.
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