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.
MediumBehavioral
0 practiced
Describe a situation where you had to tell a stakeholder 'I don't know' about an unexpected model behavior or metric. Explain how you handled the moment, the investigatory plan you proposed (timelines, data to collect), and how you maintained transparency and trust during the follow-up.
MediumTechnical
0 practiced
You're the ML engineer in a sprint with three conflicting requests: (1) a product feature requiring higher accuracy in two weeks, (2) data infrastructure asking for time to fix data quality, and (3) SRE asking to reduce model latency to meet a new SLA. Explain how you would make prioritization decisions, which factors you'd weigh (business impact, risk, effort), what trade-offs you would make, and how you'd communicate the prioritized plan to stakeholders.
HardBehavioral
0 practiced
Describe a time you pushed back on a product release deadline because of concerns about model quality or risk. Explain the evidence you gathered, how you framed the conversation with product and engineering leadership, what compromises (if any) you negotiated, and the outcome and learnings from that decision.
MediumTechnical
0 practiced
Product requires a predictive feature in two weeks, but labeled data for the target does not yet exist. Stakeholders expect a demo and a production roadmap. Outline a pragmatic plan to deliver a credible demo and propose a production plan: techniques to generate labels, validation strategies, risk communication, and timeline for moving from heuristics to ground-truth labeling.
MediumTechnical
0 practiced
Multiple models in your stack produce conflicting predictions for the same input (for example, a spam-detection model vs. a user-experience filter). How would you design an arbitration policy that balances risk, model confidence, and business rules? Discuss options such as ensembles, meta-models, confidence calibration, and deterministic overrides for high-impact cases.
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