Problem Definition and Framing Questions

Covers the skills and practices used to clarify, diagnose, and scope ambiguous business or product problems into actionable problem statements before proposing solutions. Candidates should demonstrate structured and insightful clarifying questions to understand business context, current and desired states, target users and user needs, success metrics and desired outcomes, constraints such as budget, timeline, technical dependencies, and compliance, stakeholder perspectives, and existing performance baselines. Includes separating symptoms from root causes, surfacing and testing hypotheses, identifying data to collect and analyze, performing root cause analysis, breaking complex problems into prioritized subproblems, and defining acceptance criteria and next steps or experiments to reduce uncertainty. Encompasses discovery techniques and basic user research to surface user pain points and opportunities, requirements scoping including scope boundaries, risks and trade offs, and the ability to write a concise problem statement in your own words. At senior levels also assess strategic framing, avoiding premature solutions, aligning stakeholders, and presenting an executive narrative that links diagnosis to measurable outcomes and implementation trade offs; for junior candidates emphasize curiosity, systematic thinking, and the ability to prioritize information needs rather than jumping to implementation.

HardTechnical

0 practiced

Product wants ML to detect emerging abuse types that have little or no historical labels (zero-shot). Frame the problem: list discovery techniques to surface previously unseen behavior, data collection (human-in-loop, synthetic examples), evaluation strategies for rare/novel classes, and an experimental roadmap to reduce uncertainty and prioritize labeling.

EasyTechnical

0 practiced

List and briefly explain the essential components of a concise one-paragraph ML problem statement that you would include in a PRD: what to include, what to avoid, and provide a short example (2–3 sentences) for a churn-reduction use case.

MediumTechnical

0 practiced

PM: 'Improve search relevance.' Draft eight clarifying questions you would ask in the discovery meeting. For each question, include one sentence explaining how the answer maps to potential ML or non-ML interventions (e.g., query understanding, ranking model, UX changes).

EasyTechnical

0 practiced

A PM tells you the inference endpoint must respond in under 100ms for a personalization feature. List the constraints and dependencies you would gather (hardware, network, batching, model size, caching, business SLAs, cost) and explain how each could influence model choices.

EasyTechnical

0 practiced

Describe how you would define acceptance criteria for an ML-driven 'personalized onboarding' feature intended to increase 7-day retention by 5%. Include the primary metric, secondary metrics, statistical significance requirements, rollout plan (holdout/percent-traffic), and one example of an unacceptable side effect you would guard against.

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