MLA-C02 practice question: Overfitting, underfitting and convergence
ML Model and Foundation Model (FM) Development A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A fintech analytics team trains a neural network to predict late invoice payments. Training loss keeps falling, but validation loss starts rising after about 15 epochs, and the SageMaker Debugger overfit rule that the team already runs on its training jobs has fired.
The team wants to reduce overfitting without shrinking the training data or cutting the model's capacity. The final model must come from the epoch with the best validation performance.
Which TWO changes meet these requirements? (Choose two.)
Choose two answers.
AA. Increase the number of epochs from 20 to 100, so the model has more time to fit the training data.
BB. Add dropout or weight decay regularization to the network.
CC. Raise the learning rate so that the model converges faster and reaches a lower training loss.
DD. Use early stopping that ends training when validation loss stops improving, and keep the weights from the best validation epoch.
EE. Remove the validation set so that all the data is used for training and the overfit rule stops firing.
Show the answer and explanation
Answers: B, D
A. Wrong: More epochs fit the training data further while validation loss keeps rising, which increases overfitting. The stem asks for less overfitting.
B. Right: Correct. Regularization such as dropout or weight decay discourages the network from fitting noise in the training data, which reduces the gap between training and validation loss. It keeps the same data and capacity. Learn more: List of Debugger built-in rules (opens in a new tab)
C. Wrong: A higher learning rate speeds up fitting but does not reduce the gap between training and validation loss. It can also make training unstable.
D. Right: Correct. Early stopping halts training before validation performance degrades, and keeping the best checkpoint returns the model from the epoch the stem asks for. It needs no change to the data or model size.
E. Wrong: Without a validation set, the team can no longer see overfitting, so the rule firing would be hidden rather than fixed. The model would also be chosen without validation evidence.
Explanation
Regularization like dropout or weight decay and early stopping with the best validation checkpoint both reduce overfitting while keeping the data and model capacity. Training longer, raising the learning rate or dropping validation data all make the gap between training and validation performance worse or hide it.
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