Translating Business Problems to Computational Solutions Questions
Techniques and practices for analyzing business problems, defining success criteria, and translating them into concrete computational requirements, features, user stories, and architectural decisions. Aligns business goals with product strategy, roadmap, and delivery plans; emphasizes stakeholder collaboration, scoping, prioritization, and value-driven thinking.
HardSystem Design
0 practiced
You are building a generative-AI summarization feature for legal documents. Stakeholders require provenance (source highlighting) and avoidance of hallucinations. Propose an architecture and UX pattern that provides provenance and reduces hallucination risk while keeping user experience smooth.
MediumTechnical
0 practiced
You must estimate data labeling cost for building a named-entity-recognition (NER) model across 50,000 documents with an expected average of 10 annotations per doc. Provide a cost estimation approach including time-per-annotation, QC overhead, and strategies to reduce labeling cost while preserving model quality.
EasyTechnical
0 practiced
Write a brief specification (inputs, outputs, constraints) for an API endpoint that returns a top-5 recommended product list personalized to a user. Include latency SLO, error-handling behavior, and versioning considerations that map to product release needs.
MediumTechnical
0 practiced
Evaluate the following trade-off: increasing model complexity to improve accuracy vs. keeping a simpler model to reduce maintenance and improve interpretability. Provide a decision framework that maps to product scenarios (low-stakes vs high-stakes) and describe an experiment you would run to inform the decision.
HardTechnical
0 practiced
Create a checklist of privacy, security, and governance requirements you would translate into engineering work for a healthcare AI feature that uses patient data. Include at least 8 items spanning access controls, logging, encryption, data minimization, auditability, and consent management.
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