Which component is essential for using a random subset of data as training data in Azure Machine Learning?

The essential component for using a random subset of data as training data in Azure Machine Learning is the process of splitting the data. When training a machine learning model, it's important to separate your dataset into at least two subsets: a training set and a test (or validation) set. By doing this, you ensure that the model learns from one portion of the data while being evaluated on a separate, unseen portion. This practice helps in preventing overfitting, where the model performs well on the training data but poorly on new, unseen data.

In Azure Machine Learning, the split data functionality allows you to specify how much of your dataset should be allocated for training versus testing. This is generally accomplished through various techniques such as random sampling, stratified sampling, and K-fold cross-validation. By utilizing this method, you are able to create a representative training dataset that reflects the overall distribution of the full dataset, enabling the model to generalize better.

While options like join data, feature selection, and normalization are important in different stages of data preprocessing and preparation, they do not specifically pertain to the necessity of selecting a subset of data for training purposes.