Diagnosing Overfitting: Comparing Training and Validation Accuracy
This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Based on the exhibit, what is the most likely issue with the trained model?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.”
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model with high capacity (e.g., deep neural network with many parameters) captures noise in the training data, often due to too many epochs without regularization. Techniques like dropout, L2 regularization, or early stopping are used to mitigate this, where early stopping monitors validation loss and halts training when it starts to increase, preventing the model from fitting the training data too closely.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Overfitting because training accuracy is much higher than validation accuracy — Option A is correct because 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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.
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Question Discussion
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