3. Why do you split data into training and validation sets? Data is split into two sets in order to create two models, one model with the training set and a different model with the validation set.Splitting data into two sets enables you to train the model with the training set and test the model on unseen data from the test set.Only split data when you use the Azure Machine Learning Designer, not in other machine learning scenarios.

1.Question 1The main purpose of splitting your data into a training and test sets is: 1 pointTo improve accuracyTo avoid overfittingTo improve regularizationTo improve crossvalidation and overfitting

Regarding splitting datasets into training, validation, and test partitions, which ofthe following statements is true, if any?(i) The validation set is used multiple times to choose the best value forhyperparameters.(ii) The test set is used only once to determine the performance on unseen data.(iii) Improving performance on the validation set always improves performance onthe test set.

4. What does automated machine learning in Azure Machine Learning enable you to do? Automatically deploy new versions of a model as they're trainedAutomatically provision Azure Machine Learning workspaces for new data scientists in an organizationAutomatically run multiple training jobs using different algorithms and parameters to find the best model

You're using Azure Machine Learning designer to create a training pipeline for a binary classification model. You've added a dataset containing features and labels, a Two-Class Decision Forest module, and a Train Model module. You plan to use Score Model and Evaluate Model modules to test the trained model with a subset of the dataset that wasn't used for training. What's another module should you add? Join DataSplit DataSelect Columns in Dataset

When splitting your data, what is the purpose of the training data?1 pointCompare with the actual valueFit the actual model and learn the parametersPredict the label with the modelMeasure errors