Diagnosing Overfitting: Comparing Training and Validation Accuracy
Exhibit
Refer to the exhibit. Training log from a binary classification neural network: Epoch 1/10 - loss: 1.2345, accuracy: 0.6543, val_loss: 1.4567, val_accuracy: 0.6123 Epoch 2/10 - loss: 0.9876, accuracy: 0.7123, val_loss: 1.2345, val_accuracy: 0.6543 Epoch 3/10 - loss: 0.6543, accuracy: 0.8123, val_loss: 1.0123, val_accuracy: 0.7123 Epoch 4/10 - loss: 0.4567, accuracy: 0.8765, val_loss: 0.9876, val_accuracy: 0.7345 Epoch 5/10 - loss: 0.3456, accuracy: 0.9123, val_loss: 0.9567, val_accuracy: 0.7567 Epoch 6/10 - loss: 0.2345, accuracy: 0.9456, val_loss: 0.9345, val_accuracy: 0.7789 Epoch 7/10 - loss: 0.1234, accuracy: 0.9678, val_loss: 0.9123, val_accuracy: 0.7890 Epoch 8/10 - loss: 0.0987, accuracy: 0.9789, val_loss: 0.9012, val_accuracy: 0.7912 Epoch 9/10 - loss: 0.0765, accuracy: 0.9876, val_loss: 0.8956, val_accuracy: 0.7900 Epoch 10/10 - loss: 0.0543, accuracy: 0.9932, val_loss: 0.8876, val_accuracy: 0.7890
Based on the exhibit, what is the most likely issue with the trained model?
Quick Answer
The answer is overfitting, because the training accuracy is 99.32% while the validation accuracy plateaus around 78.9%, a clear gap that signals the model has memorized the training data rather than learning to generalize. This discrepancy is the hallmark sign when diagnosing overfitting in deep learning models: the model performs exceptionally on data it has seen but fails on unseen validation data. On the CompTIA AI+ AI0-001 exam, this scenario tests your ability to compare training and validation accuracy curves—a common exhibit-based question where underfitting would show poor performance on both sets, and vanishing gradients would prevent the loss from decreasing at all. A frequent trap is mistaking high training accuracy alone for a good model; always check the validation curve. Memory tip: “High train, low valid? That’s overfit—the model’s a cheat, not a treat.”
⚠ Common exam trap
The AI0-001 exam often tests the distinction between overfitting and underfitting by presenting accuracy curves where candidates must recognize that high training accuracy with low validation accuracy indicates overfitting, not data leakage or gradient issues.
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
✓
Overfitting because training accuracy is much higher than validation accuracy
The exhibit shows a significant gap between high training accuracy and lower validation accuracy, which is the classic symptom of overfitting. The model has memorized the training data rather than learning generalizable patterns, leading to poor performance on unseen validation data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Overfitting because training accuracy is much higher than validation accuracy
Why this is correct
Training accuracy (99.32%) is significantly higher than validation accuracy (78.9%), a classic sign of overfitting.
- ✗
Data leakage artificially inflating training accuracy
Why it's wrong here
If data leakage were present, validation accuracy would also be abnormally high, but it is not.
- ✗
Vanishing gradients causing no learning
Why it's wrong here
Loss decreases steadily, so gradients are not vanishing.
- ✗
Underfitting due to insufficient epochs
Why it's wrong here
Training accuracy is very high, so the model is not underfitting.
About these practice questions
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Same concept, more angles
2 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?
hard- A.Underfitting
- B.Data leakage
- ✓ C.Overfitting
- D.Vanishing gradient
Why C: The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.
Variation 2. Based on the exhibit, what is the likely problem with the model?
medium- A.Batch size too small
- ✓ B.Overfitting
- C.Learning rate too high
- D.Underfitting
Why B: The exhibit shows training loss decreasing to near zero while validation loss increases after a certain point, which is a classic sign of overfitting. The model is memorizing the training data rather than learning generalizable patterns, leading to poor performance on unseen data.
JA
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.