- A
Implement early stopping to halt training when validation performance degrades
Early stopping monitors validation loss and stops training when it starts to increase, preventing overfitting.
- B
Add more layers to the neural network
Why wrong: Adding more layers increases model complexity and could worsen overfitting.
- C
Increase the amount of training data
Why wrong: More data can help reduce overfitting, but the immediate action to address the validation loss increase is early stopping.
- D
Increase the learning rate
Why wrong: Increasing the learning rate may cause the model to diverge and not help with overfitting.
Question 17 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 company is developing a fraud detection system using a neural network. The training loss decreases steadily but the validation loss begins to increase after a certain number of epochs. Which action should be taken to address 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
✓
Implement early stopping to halt training when validation performance degrades
The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or begins to degrade), preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or data changes.
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.
- ✓
Implement early stopping to halt training when validation performance degrades
Why this is correct
Early stopping monitors validation loss and stops training when it starts to increase, preventing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more layers to the neural network
Why it's wrong here
Adding more layers increases model complexity and could worsen overfitting.
- ✗
Increase the amount of training data
Why it's wrong here
More data can help reduce overfitting, but the immediate action to address the validation loss increase is early stopping.
- ✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate may cause the model to diverge and not help with overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS AI Practitioner often tests the distinction between overfitting (validation loss increasing) and underfitting (both losses high), leading candidates to mistakenly choose more data or more layers when the correct immediate fix is early stopping.
Detailed technical explanation
How to think about this question
Early stopping works by saving a checkpoint of the model weights whenever the validation loss achieves a new minimum (e.g., using `tf.keras.callbacks.EarlyStopping` with `restore_best_weights=True`). Under the hood, it acts as a regularizer by limiting the number of effective training iterations, which is equivalent to a form of weight decay in the optimization trajectory. In production fraud detection systems, early stopping is often combined with learning rate reduction on plateau to balance convergence speed and generalization.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement early stopping to halt training when validation performance degrades — The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or begins to degrade), preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or data changes.
What should I do if I get this AIF-C01 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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