Model Architecture Selection and Tradeoffs Questions

Deals with selecting machine learning or model architectures and evaluating relevant tradeoffs for a given problem. Candidates should explain how model choices affect accuracy, latency, throughput, training and inference cost, data requirements, explainability, and deployment complexity. The topic covers comparing architecture families and variants in different domains such as natural language processing, computer vision, and tabular data, for example sequence models versus transformer based models or large models versus lightweight models. Interviewers may probe metrics for evaluation, capacity and generalization considerations, hardware and inference constraints, and justification for the final architecture choice given product and operational constraints.

HardSystem Design

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A fintech product requires real-time interpretability for every decision: an explanation must be produced under 50ms at inference time for regulatory reasons. Propose architecture choices and explanation methods that can meet this constraint, including precomputation, surrogate models, attention-based fast explanations, and caching strategies, and discuss tradeoffs in fidelity and maintainability.

MediumSystem Design

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A real-time chat assistant must support 2,000 concurrent users on a constrained budget; you plan to use transformer models. Propose an inference architecture that balances latency and budget: discuss model distillation, multiple model sizes and routing, batching strategies, caching, autoscaling, and failover patterns to keep costs manageable while meeting latency objectives.

MediumTechnical

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For a translation service constrained by per-request compute (small CPU budget), compare classical seq2seq (RNN+attention) architectures versus transformer-based models for production. Discuss differences in inference latency, parallelization, model size, beam search implications, and when each would be preferable operationally.

HardTechnical

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Create a quantitative decision framework to score competing architecture options against business constraints: SLA, cost, explainability, time-to-market, and engineering effort. Provide a sample weighted scoring table, explain normalization and weighting choices, and describe how you would run sensitivity analysis to validate robustness of the chosen option.

HardTechnical

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Propose a hybrid architecture where small local models on-device handle latency-sensitive decisions and a centralized large model performs heavy analysis. Describe synchronization and model update strategies, how to detect and reconcile model drift between local and central models, privacy implications, and how to allocate costs and telemetry between edge and cloud.

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