Technical Problem Solving and Ownership Questions

Covers the ability to diagnose, triage, and resolve complex technical problems end to end while demonstrating personal ownership. Candidates should show deep technical reasoning about system architecture, integration complexity, data migration considerations, and custom configuration trade offs. Expect discussion of root cause analysis, diagnostic techniques, reproducible debugging, and risk mitigation strategies. Candidates should be able to explain design trade offs, propose practical solutions, assess business impact, and describe collaboration with stakeholders and cross functional teams. Emphasis should be placed on concrete actions the candidate took, how they prioritized options, and the measurable results and lessons learned.

MediumTechnical

0 practiced

You notice permutation feature importance and SHAP values shifted for several features. Design experiments to determine whether the root cause is concept drift, label noise, or label leakage. Include how you would collect control data, run ablation tests, and validate findings before retraining or rolling back.

MediumTechnical

0 practiced

Implement a Flask health-check endpoint in Python that performs three checks: 1) confirms model artifact checksum matches expected value, 2) verifies a quick feature store lookup returns a valid record, and 3) runs a lightweight inference and records latency. The endpoint should return JSON with status for each check and overall status 'ok' or 'degraded'.

HardTechnical

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You suspect an external data vendor changed upstream feature semantics and that caused model degradation. Describe how you would perform a forensic analysis to confirm causation, preserve audit trails and chain-of-custody for evidence, engage vendor and legal teams, and propose remediation and future safeguards.

MediumTechnical

0 practiced

You run an A/B experiment where Model B has higher offline validation metrics but lower online conversion than Model A. Provide a practical triage plan: list data, infra, and experiment checks to run; describe how you would reproduce the difference; and propose short-term mitigations to protect revenue while you debug.

MediumTechnical

0 practiced

How do you set SLOs, error budgets, and escalation policies for multiple ML model teams in an enterprise where models have mixed criticality? Explain the governance model, how error budgets are consumed and shared, and how escalation thresholds differ for critical models.

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