Problem Solving and Analytical Thinking Questions

Evaluates a candidate's systematic and logical approach to unfamiliar, ambiguous, or complex problems across technical, product, business, security, and operational contexts. Candidates should be able to clarify objectives and constraints, ask effective clarifying questions, decompose problems into smaller components, identify root causes, form and test hypotheses, and enumerate and compare multiple solution options. Interviewers look for clear reasoning about trade offs and edge cases, avoidance of premature conclusions, use of repeatable frameworks or methodologies, prioritization of investigations, design of safe experiments and measurement of outcomes, iteration based on feedback, validation of fixes, documentation of results, and conversion of lessons learned into process improvements. Responses should clearly communicate the thought process, justify choices, surface assumptions and failure modes, and demonstrate learning from prior problem solving experiences.

MediumTechnical

0 practiced

Model A has a higher ROC-AUC than Model B, but at the business operating threshold Model B has higher precision and lower false positives. Explain how this situation can occur, what each metric actually measures, and how you would choose which model to deploy given business constraints.

HardTechnical

0 practiced

Design an experiment to test whether a new data augmentation technique improves out-of-distribution (OOD) robustness. Specify hypotheses (null/alternative), dataset choices for OOD evaluation, metrics to measure robustness, how to perform power/sample-size calculations, and which statistical tests and corrections you'd use for multiple comparisons.

MediumTechnical

0 practiced

You lead a cross-functional incident response after an automated recommendation model exposed harmful content to users. Describe how you would coordinate technical triage, stakeholder communication (internal and external), immediate containment, investigation, and preventive actions. Include how you'd structure the postmortem and follow-through to ensure changes are implemented.

MediumTechnical

0 practiced

Compare L1 (lasso) and L2 (ridge/weight decay) regularization for neural networks. Explain the mathematical effect on optimization, the influence on sparsity and feature selection, practical trade-offs for deep learning, and scenarios where you would prefer one over the other or a combination (elastic net).

HardTechnical

0 practiced

You suspect dataset leakage is causing near-perfect training accuracy but terrible generalization. Design a methodology to detect leakage, including automated tests, data provenance checks, and model-based signals (e.g., feature importance anomalies). Provide concrete checks you would add to a CI pipeline to prevent leakage from being introduced by future data engineers.

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