Ambiguity and Scope Management Questions

Approaches for handling ill defined problems and tight time boxes by clarifying goals, bounding scope, and making testable assumptions. Skills include asking targeted clarifying questions, identifying and prioritizing unknowns and risks, decomposing large problems into manageable slices, time boxing, selecting minimal viable deliverables, explicitly stating assumptions and validation plans, and communicating trade offs to stakeholders. Also includes deciding when to gather more data versus when to proceed with pragmatic solutions and how to align expectations with partners or customers.

HardTechnical

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You're responsible for standardizing ambiguity-reduction practices across 10 AI teams. Propose a rollout plan for templated assumption documents, training sessions, and lightweight tooling that balances speed of adoption with minimal disruption. Include pilot criteria, adoption metrics, and how you'd measure improvement in scoping quality after three months.

MediumTechnical

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A product manager expects a production-ready, multi-lingual named-entity recognition (NER) model in four weeks. Outline how you would align expectations, propose a realistic scope (what to deliver in four weeks vs later), and negotiate trade-offs. Include evidence you would present (data samples, baseline results) and how you'd document the agreement to avoid later scope creep.

MediumTechnical

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You have unlabeled user logs that likely contain repeating intent patterns. Propose three small, testable heuristics to create weak labels for a fast POC intent classifier (for example, regex rules, template matching, or keyword heuristics), and explain how you would validate each heuristic's precision before using them to train a model.

HardSystem Design

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Design a deployment gating system for ML models that enforces acceptance criteria (metrics thresholds, fairness constraints, SLA). Requirements are initially vague. Propose a flexible framework to encode gates as configurable policies, describe how the CI/CD pipeline should validate gates, how to handle missing criteria, and a mechanism to evolve gates as requirements mature.

HardTechnical

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Design a practical A/B experiment to validate the assumption 'adding personalized recommendations to onboarding increases 7-day retention by 3%'. Describe treatment and control definitions, primary and secondary metrics, sample size/power calculations (show assumptions), rollout plan for a tight three-week experiment window, and early stopping rules.

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