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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.

MediumTechnical
0 practiced
An internal product owner requests a feature that requires storing additional personal identifiers. Explain how you would evaluate privacy and legal risks, propose technical mitigations (data minimization, anonymization, differential privacy, access controls), and decide whether to proceed, modify the design, or decline while aligning with company policy.
EasyBehavioral
0 practiced
Tell me about a time an AI project you worked on failed or had a major setback. Explain the root causes, how you responded operationally and emotionally, what you did to support the team, and what concrete changes you implemented afterward to improve processes or reliability.
MediumTechnical
0 practiced
Tell a story about when you changed course on an AI approach mid-project after evaluating results. Describe the evidence that prompted the pivot, how you made the decision under time pressure, how you communicated the change to stakeholders, and what lessons you carried into subsequent projects.
MediumTechnical
0 practiced
You're tasked with reducing inference latency of an image-classification model from 300ms to 50ms per request under strict GPU memory limits. Describe a step-by-step plan including profiling, model-level optimizations (quantization, pruning, architecture changes), batching considerations, serving infra options, and how you'd measure latency/accuracy trade-offs.
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.

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