- A
Increase the number of hidden layers
Why wrong: Adding more layers increases model complexity and may worsen overfitting.
- B
Add more training data
Why wrong: More data helps generalization but is often not immediately practical; early stopping is a more direct remedy.
- C
Apply early stopping with a patience of 10 epochs
Early stopping monitors validation loss and stops training when it starts increasing, directly addressing overfitting.
- D
Increase the batch size
Why wrong: Larger batch sizes can lead to sharper minima and may not prevent overfitting.
Question 637 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 machine learning engineer is tuning a neural network for image classification. The training loss decreases steadily, but the validation loss starts increasing after 50 epochs. Which action best addresses this issue?
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
✓
Apply early stopping with a patience of 10 epochs
The described behavior—decreasing training loss with increasing validation loss—is a classic sign of overfitting. Early stopping with a patience of 10 epochs directly addresses this by halting training when the validation loss fails to improve for a specified number of epochs, preventing further overfitting while retaining the best model weights.
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.
- ✗
Increase the number of hidden layers
Why it's wrong here
Adding more layers increases model complexity and may worsen overfitting.
- ✗
Add more training data
Why it's wrong here
More data helps generalization but is often not immediately practical; early stopping is a more direct remedy.
- ✓
Apply early stopping with a patience of 10 epochs
Why this is correct
Early stopping monitors validation loss and stops training when it starts increasing, directly addressing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the batch size
Why it's wrong here
Larger batch sizes can lead to sharper minima and may not prevent overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between underfitting and overfitting symptoms, and the trap here is that candidates may confuse a rising validation loss with a need for more data or a deeper network, when the correct action is to stop training early to combat overfitting.
Detailed technical explanation
How to think about this question
Early stopping works by monitoring a validation metric (e.g., loss or accuracy) and saving the model checkpoint only when that metric improves. The patience parameter defines how many epochs to wait for improvement before stopping; a patience of 10 means training continues for up to 10 epochs after the last best validation loss, then halts. This technique is a form of regularization that implicitly limits model complexity by stopping before the network overfits to noise in the training data.
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: Apply early stopping with a patience of 10 epochs — The described behavior—decreasing training loss with increasing validation loss—is a classic sign of overfitting. Early stopping with a patience of 10 epochs directly addresses this by halting training when the validation loss fails to improve for a specified number of epochs, preventing further overfitting while retaining the best model weights.
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.
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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