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.