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.

MediumTechnical

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Cache invalidation is hard: propose strategies for invalidating inference output caches when models are updated frequently and user expectations for freshness vary. Include techniques like cache versioning, TTLs, per-user invalidation, and stale-while-revalidate patterns.

EasyTechnical

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Monolith vs microservices: For an AI platform that includes data ingestion, feature engineering, model training, and inference serving, describe the pros and cons of starting with a monolithic architecture versus decomposing into microservices. Focus on developer velocity, deployment complexity, observability, and operational risk.

EasyTechnical

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You operate an image-classification inference microservice with a 200ms p95 latency SLO and expected 1,000 QPS. Would you choose synchronous (direct request→model) or asynchronous (queue + workers) architecture? Explain the reasons and how you'd architect the system to meet the SLO.

HardSystem Design

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Design a disaster recovery plan for AI workloads across regions. Cover: model artifact storage (checkpoints), feature data, in-flight requests, DNS/routing failover, testing of DR drills, and RTO/RPO targets. Explain trade-offs between hot, warm, and cold standby strategies.

HardTechnical

0 practiced

Architect serving of extremely large models (>100B parameters) for inference with acceptable latency. Discuss trade-offs among model/parameter sharding, activation offloading, quantization, caching, and using inference-specialized hardware versus managed model serving. Provide a sketch of end-to-end request flow.

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