Problem Solving Behaviors and Decision Making Questions
Covers the interpersonal and cognitive traits that shape how a candidate solves problems, including initiative, ownership, proactivity, resilience, creativity, continuous learning, and evaluating trade offs. Interviewers probe when a candidate takes initiative versus seeks help, how they balance speed versus quality, how they persist through setbacks, how they generate creative alternatives, and how they learn from outcomes. This topic assesses mindset, judgment, and the ability to make principled decisions under uncertainty.
HardTechnical
0 practiced
A junior engineer is making repeated mistakes; their manager attributes it to lack of ability while you suspect the issue stems from lack of mentorship and unclear expectations. Explain how you would investigate the situation, support the engineer with a development plan, engage the manager constructively, and make fair performance or promotion recommendations while preserving team morale.
EasyTechnical
0 practiced
Before fine-tuning a pre-trained model for a new classification task, what rapid checks and validations do you perform to determine whether the pretrained weights are suitable? Include dataset compatibility, label mapping, representation gaps, and a minimal experiment design you would run in a day or two.
MediumTechnical
0 practiced
You inherit a fragile ML pipeline with little test coverage that occasionally fails in production. Describe a 30/60/90 day action plan to triage failures, add coverage and observability, stabilize deployments, and reduce incident frequency while minimizing customer impact.
EasyBehavioral
0 practiced
Tell me about a decision you made regarding an ML model when data was incomplete or noisy. Describe what information you actively gathered, how you assessed risk and uncertainty, which assumptions you made, the decision you took, and how you validated the outcome afterward.
HardTechnical
0 practiced
Product wants aggressive personalization to increase revenue while legal and privacy teams demand strict data minimization. As AI lead mediating the conflict, present a principled approach to quantify personalization benefits versus privacy risks, propose technical compromises (on-device features, aggregation, differential privacy), governance steps, and how you'd achieve stakeholder buy-in.
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