Industry Trends and Domain Knowledge Questions

Show awareness of current trends, technical developments, and evolving best practices in a specific domain or industry vertical. For domain specialists this means being conversant with recent industry developments, major technology or methodology changes, competitive feature trends, metrics and measurement approaches, and the implications these trends have for product strategy and execution. For example, in search engine optimization candidates should know about major algorithm updates, the growing role of artificial intelligence in search, changes to ranking signals, content quality and E A T concepts, tooling and measurement techniques, and how SEO decisions affect product architecture and content strategy. Be ready to discuss how trends create opportunities and risks for companies and how you would adapt.

MediumSystem Design

0 practiced

Design a deployment decision for an LLM-powered feature with these constraints: 1M monthly requests, 200 QPS peak, strict P95 latency < 800ms, and sensitive customer data that must not be sent to third-party APIs. Compare self-hosted vs managed API across latency, operational complexity, cost, and compliance and recommend a path.

MediumTechnical

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Propose a workflow to generate synthetic data for a low-resource domain to improve NER and intent classification. Describe how you'd validate the synthetic data quality and avoid amplifying biases present in seed data.

HardTechnical

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You're asked to compare TCO of adopting an open-source LLM (self-hosted) versus continuing with a commercial API for three years. Enumerate the cost categories you would include (compute, storage, engineering, security/audit, licensing), describe assumptions you'd need, and sketch a template calculation one could use to decide.

EasyTechnical

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Explain Retrieval-Augmented Generation (RAG) in the context of building knowledge-driven features. Describe the main components (document store, retriever, reader/LLM), typical latency trade-offs, and one scenario where RAG is preferable to using parametric knowledge inside an LLM.

MediumTechnical

0 practiced

Describe the architecture and components of a production-ready model performance monitoring pipeline. Include what signals to compute (latency, error rates, calibration, input distribution), how to store metrics, alerting policy, and one plan for automated rollback or routing to a safe model.

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