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.

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.

HardSystem Design

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You have a model that needs to serve 10k QPS with 95th-percentile latency <50ms. Provide a capacity planning sketch: how many replicas, expected CPU/memory per replica, caching strategy, and how you'd validate this plan with load testing. State assumptions.

MediumTechnical

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You observe that a recently deployed model causes increased tail latency and occasional request timeouts. Outline a troubleshooting checklist for the data scientist and engineering team to diagnose and fix the issue (include monitoring signals, quick rollbacks, and long-term fixes).

MediumTechnical

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A business asks for 99.99% prediction availability across their primary market. As a data scientist collaborating with engineers, what architectural decisions influence this SLA and which patterns (e.g., replication, circuit breakers, graceful degradation) would you recommend to meet it?

HardSystem Design

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Design a monitoring and alerting plan for model-serving infrastructure that covers both system health and model quality. List key metrics, alert thresholds, anomaly detection strategies, and on-call responsibilities you would recommend.

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