When a model is evaluated on the training data, it simply remembers the whole training set and always predicts correctly. This is known as overfitting.
The source passage explicitly states: "our model will simply remember the whole training set, and will therefore always predict the correct label for any point in the training set. This is known as overfitting." Examiners expect the exact term overfitting as the answer.