AI0-001 AI Models and Data Engineering Practice Question
A data scientist trains a deep learning model on a large dataset. The training loss decreases steadily but the validation loss starts increasing after 20 epochs. The scientist uses early stopping with patience=5. Which of the following is the MOST likely cause and best corrective action?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and underfitting by showing a diverging validation loss curve, and the trap here is that candidates may confuse overfitting with a learning rate issue or data quality problem, leading them to choose 'reduce learning rate' or 'collect more data' instead of the correct regularization technique.
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
✓
Model is overfitting; add dropout regularization.
The training loss decreasing while validation loss increasing after 20 epochs is a classic sign of overfitting, where the model memorizes training data noise instead of generalizing. Early stopping with patience=5 would halt training after 5 epochs of no validation improvement, but the root cause is overfitting. Adding dropout regularization randomly drops neurons during training, forcing the network to learn more robust features and reducing overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Model is overfitting; add dropout regularization.
Why this is correct
Diverging validation loss after training loss decrease is classic overfitting; dropout helps.
- ✗
Training data is not representative; collect more data.
Why it's wrong here
Data issues may cause poor validation but overfitting is more directly indicated by the loss pattern.
- ✗
Model is underfitting; increase model capacity.
Why it's wrong here
Underfitting shows high training loss, not decreasing loss.
- ✗
Learning rate too high; reduce learning rate.
Why it's wrong here
High learning rate often prevents convergence, not a typical cause of validation loss divergence after initial decrease.
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.