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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.

MediumTechnical
59 practiced
Model accuracy has dropped by 8% after a recent product release. As the on-call ML engineer, list structured clarifying questions and a prioritized investigative checklist to determine whether the regression is caused by data drift, schema changes, model bug, or product behavior change.
EasyTechnical
106 practiced
You are asked to propose success metrics for an email spam classifier feature. Describe at least five metrics (including business-oriented metrics), explain the trade-offs between them (e.g., precision vs. recall), and provide a short example of an acceptance threshold that ties to business impact.
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.
HardTechnical
61 practiced
The board requests an 18-month ML roadmap with expected ROI and risk for each initiative. Describe how you would derive ROI estimates (top-down and bottom-up), quantify uncertainty and sensitivity, categorize risks (data, modeling, regulatory, adoption), and present a clear recommendation with contingency plans for the board.
MediumTechnical
104 practiced
Given a dataset schema and monthly sample sizes for an app, explain how to estimate whether you have enough data to train a 30-day retention prediction model. Describe the calculations or rules-of-thumb you would use (class balance, MDE, number of features), and what additional data you would request if the estimate is insufficient.

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