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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
Estimate data storage, GPU compute-hours, and cost for training a transformer model on 100 million short text samples with average length 50 tokens. State your assumptions about batch size, epochs, model size (params), and how these assumptions would change scope and timeline for the project.
HardTechnical
0 practiced
Design a requirement checklist and acceptance criteria for launching an AI system that must comply with HIPAA. Cover data flows, encryption at rest and in transit, audit logging, de-identification, access control, third-party processing controls, incident detection and breach response, and how you would technically validate each item before go-live.
MediumSystem Design
0 practiced
Design a phased rollout plan for a new ML feature: pilot with 1% of users, then 10%, then 100%. For each phase list instrumentation points, gating criteria to proceed to the next phase, rollback triggers, and communication steps for stakeholders.
EasyTechnical
0 practiced
Define 'acceptance criteria' in the context of an AI feature. Then write a concise set of acceptance criteria (3-6 items) for an MVP that automatically summarizes customer support emails into a one-paragraph summary suitable for an agent. Make each criterion measurable and indicate the primary test that would validate it.
MediumTechnical
0 practiced
What data governance and lineage requirements would you elicit before integrating a paid third-party dataset into training pipelines? Provide acceptance criteria for provenance, licensing checks, data freshness, and automated lineage tests.

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