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.
EasyTechnical
0 practiced
How would you structure technical specifications so engineers and product owners can trace work from a business objective to data tables, feature code, model artifacts, tests, and deployment? Provide a recommended document structure and examples of necessary fields for traceability and reproducibility.
MediumTechnical
0 practiced
Design a step-by-step implementation plan for a churn prediction project whose goal is to reduce churn by 5% next quarter. Include milestones, data requirements, model candidate types, validation plan, deployment strategy (pilot/rollout), rollback criteria, and how you will measure business impact. Assume data is available from CRM, transactions, and web logs.
MediumTechnical
0 practiced
Design an experiment prioritization matrix for a product team with 50 feature ideas. Specify dimensions (e.g., expected impact, ease of implementation, confidence, reach), scoring method, and provide example scores for three sample ideas: A) promotional banner personalization, B) new checkout flow, C) database indexing for reporting. Explain how you'd use the matrix to pick the next 5 experiments.
HardSystem Design
0 practiced
You need to build a real-time fraud detection system handling 5,000 TPS and blocking fraud within 200ms. Stakeholders are vague on acceptable false positives. Decompose the requirement into measurable components: throughput/latency, acceptable FP/TP trade-offs, data schema for streaming features, architecture options (feature store vs feature cache), model serving choices, rate-limiting strategies, monitoring, rollback, and a stakeholder decision process for setting FP/TP thresholds.
MediumTechnical
0 practiced
Given this table schema: orders(order_id PK, user_id INT, sku TEXT, price DECIMAL, status TEXT, created_at TIMESTAMP), write SQL queries to: 1) compute weekly active users (WAU), 2) compute weekly revenue per cohort where cohort is week of first purchase, and 3) write a data-quality query to detect duplicate orders (same user_id, sku, created_at within 1 minute). Include assumptions and brief explanation for each query. Use standard ANSI SQL.
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