The question requires a hyperparameter sampling method that selects new samples based on the performance of previous samples. Bayesian sampling is the optimal choice because it uses Bayesian optimization to build a probabilistic model of the objective function and selects the next hyperparameter values based on previous evaluation results, making it adaptive and efficient. Grid sampling tests all possible combinations in a predefined grid without considering previous performance, making it inefficient. Random sampling selects hyperparameters randomly without leveraging past performance information, which is less effective for optimization. The community discussion shows 100% consensus on answer B, with references to Microsoft documentation confirming Bayesian sampling as the appropriate method for this adaptive sampling requirement.