A . Feature selection

Feature selection is the process of selecting the most relevant features from the data. While

important, it is not directly about handling excess data.

B . Data sampling

Data sampling involves selecting a representative subset of the data for training. When there is more

data than needed, sampling can be used to create a manageable dataset that maintains the

statistical properties of the full dataset.

C . Data labeling

Data labeling involves annotating data for supervised learning. It is necessary for training models but

does not address the issue of having excess data.

D . Data augmentation

Data augmentation is used to increase the size of the training dataset by creating modified versions

of existing data. It is useful when there is insufficient data, not when there is excess data.

Therefore, the correct answer is B because data sampling is the most relevant activity when dealing

with an excess amount of data for training​​.