AI0-001 Machine Learning and Deep Learning Practice Question
A machine learning engineer is tuning a neural network for image classification. The training loss decreases steadily, but the validation loss starts increasing after 50 epochs. Which action best addresses this issue?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between underfitting and overfitting symptoms, and the trap here is that candidates may confuse a rising validation loss with a need for more data or a deeper network, when the correct action is to stop training early to combat overfitting.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply early stopping with a patience of 10 epochs
The described behavior—decreasing training loss with increasing validation loss—is a classic sign of overfitting. Early stopping with a patience of 10 epochs directly addresses this by halting training when the validation loss fails to improve for a specified number of epochs, preventing further overfitting while retaining the best model weights.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of hidden layers
Why it's wrong here
Adding hidden layers increases model capacity, which worsens the widening gap between falling training loss and rising validation loss. It is tempting because extra layers help underfitting, where both losses stay high, but here the network already fits the training data too closely.
- ✗
Add more training data
Why it's wrong here
More training data can reduce overfitting eventually, but it does not directly counter the divergence already occurring after 50 epochs. It is tempting because larger datasets generally improve generalisation, yet the immediate fix for validation loss rising while training loss falls is regularisation or early stopping.
- ✓
Apply early stopping with a patience of 10 epochs
Why this is correct
Early stopping with patience halts training once validation loss stops improving for 10 consecutive epochs, directly countering the overfitting that begins after epoch 50. It preserves the best-performing weights rather than continuing to minimise training loss, satisfying the stem's requirement to address rising validation loss.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size changes gradient averaging and can smooth training, but it does not stop the model memorising the training set, so validation loss keeps rising. It is tempting because larger batches stabilise noisy gradients, which helps when training loss itself is erratic rather than diverging from validation.
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.