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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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.

HardSystem Design

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Design monitoring, alerting and automated mitigation for model performance regressions and data drift. Include what signals trigger automatic rollback, how to separate signal noise from true degradation, and a playbook for human-in-the-loop investigation.

HardTechnical

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Provide a numeric cost vs performance analysis for serving an LLM: compare (A) many small instances with quantized CPU inference achieving 50ms p95 but lower throughput vs (B) fewer GPU instances with 10ms p95 but higher per-hour cost. Given sample numbers, show how you'd compute cost per 1M requests and reason which to pick for different business priorities.

EasyTechnical

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Technical: Implement a thread-safe token-bucket rate limiter in Python that supports burst capacity, refill rate (tokens/sec), and a non-blocking `allow_request(key)` API suitable for per-user inference throttling. Explain assumptions and how you'd extend this to a distributed environment.

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.

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