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
0 practiced
Design a globally distributed inference architecture for a foundation-model-based NLP API with peak 100k QPS and a P99 latency target <100ms worldwide. Address model placement (regional replicas vs edge), geo-routing, cold-start and warm pool strategies, caching, cost optimization, version consistency across regions, telemetry collection, and failover considerations.
MediumSystem Design
0 practiced
Design a CI/CD pipeline that supports evaluating multiple candidate architectures, automated regression detection, model validation, performance testing, and staged rollout into production. Describe components needed (reproducible training artifacts, validation infra, metrics store, canary serving) and how automation enforces decision gates prior to promotion.
MediumTechnical
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You have only 5,000 labeled images for a new object recognition task. Compare strategies that trade model size against data volume: (1) small custom model, (2) transfer learning with pretrained backbone, (3) heavy data augmentation and synthetic data, and (4) active learning. For each, describe expected accuracy gains, annotation or compute costs, and operational overhead.
MediumSystem Design
0 practiced
You design a critical API whose responses are driven by an ML model. Define fallback strategies when the model is unavailable, returns low-confidence predictions, or violates SLAs. Consider rule-based fallbacks, cached responses, graceful degradation of UX, circuit-breakers, and how you would implement and test these strategies in architecture.
MediumTechnical
0 practiced
You are selecting architectures for a binary classification problem with severe class imbalance (1% positives). Describe model-level choices (loss functions, calibration), data-level approaches (resampling, synthetic generation), architecture families that handle imbalance well, and evaluation metrics that reflect business risk.
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