- A
Underfitting; increase model complexity
Why wrong: Underfitting would have high training loss, not decreasing then validation loss increasing.
- B
Vanishing gradients; use ReLU activation
Why wrong: Vanishing gradients cause slow learning, not validation loss increase.
- C
Data leakage; shuffle data before splitting
Why wrong: Data leakage would lead to unrealistically high performance, not a validation loss increase.
- D
Overfitting; apply dropout and early stopping
Validation loss increasing while training loss decreases is classic overfitting; dropout regularizes and early stopping halts training.
Question 351 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 data scientist is training a deep neural network for sentiment analysis. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. What is the most likely cause and best corrective action?
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
✓
Overfitting; apply dropout and early stopping
The scenario describes a classic case of overfitting: the training loss decreases steadily, indicating the model is learning the training data well, but the validation loss increases after 10 epochs, meaning the model is memorizing noise and patterns specific to the training set rather than generalizing. The best corrective action is to apply dropout (which randomly drops neurons during training to reduce co-adaptation) and early stopping (which halts training when validation performance degrades), both of which are standard regularization techniques for deep neural networks.
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.
- ✗
Underfitting; increase model complexity
Why it's wrong here
Underfitting would have high training loss, not decreasing then validation loss increasing.
- ✗
Vanishing gradients; use ReLU activation
Why it's wrong here
Vanishing gradients cause slow learning, not validation loss increase.
- ✗
Data leakage; shuffle data before splitting
Why it's wrong here
Data leakage would lead to unrealistically high performance, not a validation loss increase.
- ✓
Overfitting; apply dropout and early stopping
Why this is correct
Validation loss increasing while training loss decreases is classic overfitting; dropout regularizes and early stopping halts training.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between underfitting and overfitting by describing a diverging validation loss after initial improvement, leading candidates to mistakenly choose underfitting or vanishing gradients when the key indicator is the validation loss increase after a period of good training loss reduction.
Detailed technical explanation
How to think about this question
Overfitting in deep neural networks often occurs when the model capacity (number of parameters) exceeds the amount of training data, allowing the network to fit noise. Dropout works by randomly setting a fraction of neuron outputs to zero during each forward pass, effectively training an ensemble of sub-networks and reducing reliance on specific features; early stopping monitors validation loss and halts training at the point of best generalization, preventing the model from entering the overfitting regime. In practice, a validation loss increase for several consecutive epochs (e.g., 5) is a common early stopping criterion.
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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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: Overfitting; apply dropout and early stopping — The scenario describes a classic case of overfitting: the training loss decreases steadily, indicating the model is learning the training data well, but the validation loss increases after 10 epochs, meaning the model is memorizing noise and patterns specific to the training set rather than generalizing. The best corrective action is to apply dropout (which randomly drops neurons during training to reduce co-adaptation) and early stopping (which halts training when validation performance degrades), both of which are standard regularization techniques for deep neural networks.
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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