Requirements Analysis & Problem Decomposition Questions

Break down complex business requirements into smaller technical components. Identify ambiguities and ask clarifying questions. Prioritize requirements logically. Plan implementation approach step by step. Create technical specifications from business requirements.

MediumTechnical

71 practiced

Stakeholder asks to 'improve recommendation relevance.' Propose a measurable objective with specific offline evaluation metric(s), an online experiment design (A/B or multi-armed), and success thresholds. Explain the trade-offs between using offline metrics (e.g., NDCG) and online metrics (e.g., CTR, revenue).

EasyTechnical

83 practiced

Estimate the time required for this mid-size project with one data scientist: data cleaning (10M rows with multiple join keys), feature engineering, baseline model development, and documentation. Provide a breakdown by task and the assumptions you make about tooling, data quality, and team experience.

EasyTechnical

87 practiced

You have only two weeks and one data engineer to produce a minimal viable product (MVP) for a binary classification that flags likely fraudulent accounts. Propose a scoped MVP: data sources, minimal feature set, model family, deployment strategy (demo vs production), and success criteria for the 2-week timeline.

HardTechnical

99 practiced

You must prioritize a portfolio of ten ML initiatives with limited engineering and compute resources. Define a set of criteria (e.g., revenue impact, time-to-value, legal risk, strategic alignment), a scoring mechanism, and show example scoring and ranking for three projects: 1) high revenue, low complexity; 2) moderate revenue, high legal risk; 3) low revenue, critical ops automation. Explain how interdependencies and sunk costs affect prioritization.

EasyTechnical

73 practiced

A stakeholder requests 'real-time scoring' for a fraud pipeline. List the clarifying SLA questions you would ask, covering latency targets, throughput (TPS), acceptable false-positive rate, statefulness, data freshness, auditing, and data retention. Explain how each clarification alters the solution design.

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