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

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A streaming feature pipeline produces aggregates with 500ms processing latency. Your model requires features no older than 1 second for real-time decisions. Describe how you'd architect backpressure handling, and how you'd prevent an upstream slowdown from causing unbounded latency spikes in predictions.

EasyTechnical

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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

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You're choosing between two data transport patterns for events feeding feature computation: (1) at-least-once via a distributed log (e.g., Kafka) or (2) exactly-once streaming with heavier transactional guarantees. For a churn-prediction pipeline where duplicate events could bias counts, which would you pick and why? Discuss cost and operational complexity.

EasyTechnical

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A data scientist is asked to choose between using an in-memory cache (e.g., Redis) versus relying on a fast database for serving feature vectors to a model. List the practical pros and cons of using a cache for features in production.

HardTechnical

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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.

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