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.

HardTechnical

0 practiced

As a staff AI engineer, you must establish a reproducible experiments and model registry policy across research and production teams. Outline the policy elements (metadata, dataset snapshots, code versions, seeds), processes (CI gating, promotion criteria), and enforcement mechanisms (automation, audits). Explain how to balance researcher flexibility against production safety and compliance.

EasyTechnical

0 practiced

You are building a real-time model-monitoring system that ingests streaming predictions and needs to compute running statistics without storing all historical values. Implement a Python class with methods add(value) and get_mean(), get_variance() that uses a one-pass, numerically stable online algorithm (Welford's method). Your implementation should handle initial values, very large values, and allow merging statistics computed on separate processes.

MediumTechnical

0 practiced

You are evaluating a multi-label classification problem with imbalanced labels and rare critical classes. Design an evaluation pipeline: indicate which metrics you would report (macro/micro, per-class metrics), sampling strategies for validation, and how to prioritize improvements (e.g., focus on recall for certain rare classes). Also discuss threshold selection for each label.

EasyTechnical

0 practiced

Your monitoring system shows drift in model predictions. An upstream schema change is suspected (field renamed/typed differently). Design a small, practical experiment to verify whether the schema change caused the prediction drift. Include the data you would collect, specific statistical tests or visualizations, and how you'd safely roll back or mitigate while investigating.

MediumTechnical

0 practiced

A new text-generation model release increased hallucination rate by 10% compared to the prior version. Create a diagnostic plan covering (1) data differences (fine-tuning data), (2) model differences (capacity, decoding parameters), (3) serving differences (tokenizer changes, temperature), and (4) human annotation strategies to quantify and triage issues. Propose immediate mitigations and long-term fixes.

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