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You create a binary classification model by using Azure Machine Learning Studio. You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements: * iterate all possible combinations of hyperparameters * minimize computing resources required to perform the sweep * You need to perform a parameter sweep of the model. Which parameter sweep mode should you use?
Correct Answer: D
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling. If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.