Exam DP-100 topic 3 question 142 discussion

answer is c.choice

Grid sampling can only be used with choice hyperparameters. Reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2#grid-sampling

C For configuring a grid sampling method over the search space for hyperparameters like num_hidden_layers and batch_size, you should use the "choice" hyperparameter sampling approach. The "choice" sampling approach allows you to specify a list of discrete values from which the hyperparameter tuning process will select combinations. In your case, you would provide a list of possible values for num_hidden_layers and batch_size, and the grid search would consider all combinations of these values. So, the correct option is: C. choice

Choice

from azureml.train.hyperdrive import choice, GridParameterSampling param_sampling = GridParameterSampling({ 'num_hidden_layers': choice([16, 32, 64]), 'batch_size': choice([32, 64, 128]) })

c) Choice https://learn.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.gridparametersampling?view=azure-ml-py

Grid sampling does a simple grid search over all possible values. Grid sampling can only be used with choice hyperparameters. For example, the following space has six samples: