A sanitation department wants many hyperparameter trials for a route-optimization model but must control Azure spend. Which sweep practice should they apply?

Parallelizing hyperparameter trials with early termination to stop poor runs and control cost is the correct sweep practice for a sanitation route-optimization model under Azure spend constraints. Parallelism shortens wall-clock search time; early termination (bandit-style or metric-threshold policies) cancels clearly inferior trials before they burn full epochs. Together they maximize learning per dollar—exactly what municipal MLOps needs when many hyperparameters compete for limited budget.

Applying grid sampling with fixed trial budgets to complete full runs and control cost fails because it exhaustively tests combinations without early termination, so poor configurations still consume their full budget and drive up Azure spend. Expanding random sampling concurrency to trial limits to shorten search time and control cost fails because broader random search plus high concurrency still lets unpromising trials run to completion, multiplying wasted spend instead of stopping poor runs early. Applying Bayesian sampling with sequential scheduling to refine good runs iteratively and control cost fails because sequential scheduling runs trials one after another, lengthening wall-clock time and forgoing the parallelism needed to finish many trials quickly under a spend cap.

Exam caveat: aggressive early stopping can discard slow-starting but ultimately strong configurations—validate termination policy against the training curve shape of the route model. Concurrency caps must fit workspace quota and shared civic compute. Operational check: configure a sweep with a defined search space, set max concurrent trials and an early-termination policy, monitor cost and primary metric, and keep only promising trials for full training before sanitation promotes a champion.