Architecture and Technical Trade Offs Questions

Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.

HardSystem Design

34 practiced

A dataset contains PII fields required for model features, but regional regulations restrict storing PII in certain regions. Propose architectures to serve models globally while satisfying data residency constraints and discuss trade-offs for latency, engineering effort, and model performance.

HardTechnical

26 practiced

Compare two caching strategies for ML feature lookups: (A) write-through cache updated synchronously on feature write, and (B) lazy cache populated on first read. For a heavy-write, read-light feature with strict freshness, which is preferable? Quantify trade-offs in terms of write latency, read latency, consistency, and cost.

EasyTechnical

30 practiced

Explain what a feature store is and the differences between online and offline feature stores. As a data scientist, when would you prioritize building an online feature store?

MediumTechnical

27 practiced

Design a plan to A/B test a new model architecture that requires additional upstream features with higher cost. How would you allocate traffic, measure business impact vs infrastructure cost, and roll out after success? Include rollback criteria.

MediumTechnical

47 practiced

Design a lightweight prototype to compare cold-start times between two model serving frameworks. Describe experiment setup, metrics to capture (including warm-up behavior), how to control for noise, and criteria to declare a winner.

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