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

85 practiced

Executive-level ask: build an "AI-driven personalization" capability across product. Prepare a one-page executive narrative outline that frames the problem, lists measurable outcomes (with sample KPIs), compares three strategic options (build, buy, partner) with pros/cons, identifies top risks and mitigations, and recommends next steps and timelines. Describe the headings and 2–3 bullet points you would include under each heading.

MediumSystem Design

62 practiced

Design an A/B test to validate a new ML model intended to reduce cart abandonment. Specify: the primary metric, secondary/guardrail metrics, how you would randomize, treatment assignment rules, a high-level approach to compute minimum detectable effect (MDE), expected experiment duration considerations, and rollout steps for progressive launch.

EasyTechnical

51 practiced

Explain the difference between a symptom and a root cause in product metrics, using customer churn as an example. Describe a concise 3-step process you would use to move from observed symptom (higher churn) to candidate root causes and what data checks would support each step.

MediumTechnical

84 practiced

You observe a 15% drop in new user activation over the last quarter. As the ML engineer asked to evaluate whether ML can help, outline a structured diagnostic plan: list which datasets and segments to analyze, top hypotheses to test, quick offline analyses, and what would be a minimum success criterion to proceed with a model.

MediumTechnical

66 practiced

You must decide whether to build an ML model or a rules-based system for content moderation. List decision criteria you would evaluate (data, performance, explainability, maintenance cost, latency), what data checks matter most, and three scenarios where rules are preferable to ML and vice versa.

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