- A
Vanishing gradient; use ReLU activation
Why wrong: Vanishing gradient typically stalls learning, not a later divergence.
- B
Overfitting; apply regularization like dropout
Dropout randomly drops neurons to prevent co-adaptation, reducing overfitting.
- C
Underfitting; increase model complexity
Why wrong: Underfitting would show high loss on both sets; here training loss is low.
- D
Data leakage; reshuffle split
Why wrong: Data leakage would cause overly optimistic training but not necessarily later validation increase.
Question 894 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. 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.
A team trained a deep neural network on a limited dataset. The training loss decreases consistently, but the validation loss starts increasing after 20 epochs. What is the most likely issue and the 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 regularization like dropout
The training loss decreasing while validation loss increasing after 20 epochs is the classic signature of overfitting: the model has memorized the training data but fails to generalize to unseen data. Applying regularization like dropout forces the network to learn more robust features by randomly dropping neurons during training, reducing overfitting. This is the most direct and effective corrective action for this specific symptom.
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.
- ✗
Vanishing gradient; use ReLU activation
Why it's wrong here
Vanishing gradient typically stalls learning, not a later divergence.
- ✓
Overfitting; apply regularization like dropout
Why this is correct
Dropout randomly drops neurons to prevent co-adaptation, reducing 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.
- ✗
Underfitting; increase model complexity
Why it's wrong here
Underfitting would show high loss on both sets; here training loss is low.
- ✗
Data leakage; reshuffle split
Why it's wrong here
Data leakage would cause overly optimistic training but not necessarily later validation increase.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between overfitting and vanishing gradients by showing a loss curve that decreases initially then rises, tricking candidates into thinking the gradient is vanishing when the real issue is poor generalization.
Trap categories for this question
Command / output trap
Underfitting would show high loss on both sets; here training loss is low.
Detailed technical explanation
How to think about this question
Dropout works by randomly setting a fraction of neuron outputs to zero during each forward pass (e.g., 0.5 for hidden layers), effectively training an ensemble of sub-networks and preventing co-adaptation of features. In practice, the validation loss curve often starts to rise after the model begins to fit noise in the training data—this inflection point is where early stopping could also be applied as an alternative corrective action. Real-world scenarios like medical image diagnosis with small datasets frequently encounter this exact behavior, making regularization techniques critical.
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 regularization like dropout — The training loss decreasing while validation loss increasing after 20 epochs is the classic signature of overfitting: the model has memorized the training data but fails to generalize to unseen data. Applying regularization like dropout forces the network to learn more robust features by randomly dropping neurons during training, reducing overfitting. This is the most direct and effective corrective action for this specific symptom.
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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