AdMarketInc runs a recommendation model training on a single p3.16xlarge that takes ~12 hours per trial. They have a strict budget and can run at most ~20 full training trials. Model evaluations are moderately noisy but each trial is expensive. Which SageMaker Automatic Model Tuning strategy should they choose to most efficiently find a near-optimal hyperparameter configuration within their limited trial budget?

Bayesian optimization is the most suitable strategy here because it is designed for situations with expensive, noisy evaluations and a limited budget. It intelligently explores the hyperparameter space by building a probabilistic model (Gaussian Process) of the objective function. This allows it to prioritize evaluating hyperparameter combinations that are most likely to improve performance, leading to faster convergence to a near-optimal solution with fewer trials (sample-efficient). Random search is generally robust but less efficient than Bayesian optimization for expensive trials. HyperBand is excellent for reducing training time by early stopping poor-performing trials, but the question emphasizes expensive evaluations and a fixed budget of full trials, not necessarily reducing individual trial runtimes. Grid search is exhaustive and computationally prohibitive for many hyperparameters, quickly exceeding the 20-trial budget.