- A
The validation set is too small
Why wrong: 15% of 100k is 15k records, sufficient for reliable validation.
- B
The model generalizes well
Why wrong: Generalization would show similar training and validation performance.
- C
The model is overfitting
Overfitting explains high training accuracy and lower validation accuracy.
- D
The test set should be larger
Why wrong: Test set size does not affect training-validation comparison.
Question 536 of 1,000
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A machine learning engineer has a dataset of 100,000 records. She splits it into 70% training, 15% validation, and 15% test sets. After training, the model achieves 95% accuracy on training and 85% on validation. What does the accuracy difference most likely indicate?
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.
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
✓
The model is overfitting
The 10% gap between training accuracy (95%) and validation accuracy (85%) is a classic sign of overfitting. The model has memorized patterns specific to the training set rather than learning generalizable features, causing it to perform worse on unseen validation data. In machine learning, a significant drop in performance from training to validation indicates poor generalization, which is the hallmark of overfitting.
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.
- ✗
The validation set is too small
Why it's wrong here
15% of 100k is 15k records, sufficient for reliable validation.
- ✗
The model generalizes well
Why it's wrong here
Generalization would show similar training and validation performance.
- ✓
The model is overfitting
Why this is correct
Overfitting explains high training accuracy and lower validation accuracy.
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.
- ✗
The test set should be larger
Why it's wrong here
Test set size does not affect training-validation comparison.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between overfitting and data split issues, trapping candidates who mistake a performance gap for an insufficient validation set rather than recognizing it as a model generalization problem.
Trap categories for this question
Similar concept trap
Generalization would show similar training and validation performance.
Command / output trap
Generalization would show similar training and validation performance.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model learns noise and idiosyncrasies in the training data, often due to high model complexity (e.g., too many parameters or deep layers) relative to the dataset size. Techniques like regularization (L1/L2), dropout, or early stopping can mitigate this by penalizing large weights or reducing model capacity. In practice, cross-validation and monitoring the validation loss curve during training help detect overfitting before it degrades test performance.
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.
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Machine Learning and Deep Learning — study guide chapter
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FAQ
Questions learners often ask
What does this AI0-001 question test?
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: The model is overfitting — The 10% gap between training accuracy (95%) and validation accuracy (85%) is a classic sign of overfitting. The model has memorized patterns specific to the training set rather than learning generalizable features, causing it to perform worse on unseen validation data. In machine learning, a significant drop in performance from training to validation indicates poor generalization, which is the hallmark of overfitting.
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.
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Last reviewed: Jul 4, 2026
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