Requirements Elicitation and Scoping Questions

This topic covers the end to end practice of clarifying ambiguous problem statements, eliciting and defining functional and non functional requirements, and scoping solutions before design and implementation. Candidates should demonstrate the ability to identify target users and user journeys, conduct stakeholder interviews, ask targeted and probing clarifying questions, surface hidden assumptions and root causes, and convert vague business language into measurable technical and business requirements. They should capture acceptance criteria and success metrics, define key performance indicators, and translate requirements into testable statements and test strategies that map unit, integration, and system tests to requirement risk and priority. The topic includes assessing technical constraints and operational context such as expected scale, throughput and latency requirements, data volume and read write ratios, consistency expectations, real time versus batch processing trade offs, geographic distribution, uptime and availability expectations, security and compliance obligations, and existing system state or migration considerations. It also requires evaluation of non technical constraints including timelines, team capacity, budget, regulatory and operational concerns, and stakeholder priorities. Candidates are expected to synthesize inputs into clear artifacts such as product requirement documents, user stories, prioritized backlogs, acceptance criteria, and concise requirement checklists to guide architecture, estimation, and implementation. Emphasis is placed on scoping and prioritization techniques, distinguishing must have from nice to have features, conducting trade off analysis, proposing incremental or phased approaches, identifying risks and mitigations, and aligning cross functional teams on scope and success measures. Expectations vary by seniority: entry level candidates should reliably ask core clarifying questions and avoid solving the wrong problem, while senior and staff candidates should rapidly prioritize requirements, anticipate critical non functional needs, align solutions to business impact, and communicate trade offs and timelines to stakeholders.

MediumTechnical

0 practiced

A PRD claims 'improve revenue by 5% using model-powered recommendations.' Describe how you would translate this high-level claim into measurable model objectives, list confounders you must control for, and design experiments and attribution methods (including instrumentation) to validate causality and quantify lift.

HardSystem Design

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Define a robust end-to-end testing and validation strategy to prevent regressions when models are retrained nightly. Include unit tests for data transforms, automated data validation, model performance gates, shadow deployments, canary metrics and thresholds, rollback rules, and how each test maps to requirement risk and priority.

HardTechnical

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For a health-care ML product operating under HIPAA and GDPR, enumerate the non-functional requirements you would include in the PRD (data residency, encryption-at-rest/in-transit, access controls/role-based access, audit trails, model interpretability, retention and deletion policies). Explain how you would validate compliance and how these requirements would change scoping and timelines.

EasyTechnical

0 practiced

As an entry-level MLE you are given: 'Use historical user events to predict churn.' List eight clarifying data questions you would ask to avoid solving the wrong problem. Cover: precise churn definition, labeling window, lookback periods, feature freshness, sampling biases, missing data handling, PII constraints, and validation split strategy.

EasyTechnical

0 practiced

Explain the difference between functional and non-functional requirements in the context of an ML product. Provide three concrete examples of each for a fraud detection model (e.g., model scoring behavior, latency SLO, auditability), explain why each example matters for scoping, and how it affects implementation choices.

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