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.

MediumSystem Design

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Design a CI/CD pipeline for ML models: include unit tests, dataset validation, model training triggers, model evaluation/gating, model registry promotion, and automated canary deploys. What guardrails prevent unsafe or low-quality models from reaching production?

EasyTechnical

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Explain dynamic batching for GPU-based model inference: how it works, when it reduces cost, and what the trade-offs are for tail latency and throughput. Provide a simple algorithmic sketch for a batching scheduler that obeys per-request latency SLOs while maximizing batch fill.

HardSystem Design

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Design a scheduler for a shared GPU cluster that optimizes for both latency-critical inference and throughput-oriented training jobs. Include preemption, job migration, checkpointing policies, and how fairness is enforced while meeting SLAs.

HardTechnical

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Evaluate trade-offs between using managed cloud AI inference services (e.g., SageMaker, Vertex AI) vs self-managing inference infrastructure for a fast-growing startup that needs to balance speed-to-market, cost, and control. Include migration, locking risks, and monitoring/observability differences.

MediumTechnical

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Prototyping & measurement: Given a choice between two architectures for inference (A: many small GPUs with dynamic batching; B: fewer large GPUs with efficient batching), describe an experiment plan to measure which is better for your workload. Include metrics, required load-patterns, cost measurement, and success criteria.

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