hardMultiple Choice
AIF-C01 Practice Question: Developing a fraud detection system using a…
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?
⚠ Common exam trap
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.
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.
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 each epoch and halts training once it stops improving, directly countering the divergence described. It preserves the weights from the best-performing epoch, preventing the overfitting that causes validation loss to rise while training loss keeps falling. This satisfies the stem's requirement without altering the model architecture.
- ✗
Add more layers to the neural network
Why it's wrong here
Adding layers increases model capacity, letting the network fit training noise more tightly and widening the train-validation gap. It is tempting because extra depth helps when both losses remain high, indicating underfitting, and would be correct if the model were too simple to capture the fraud patterns.
- ✗
Increase the amount of training data
Why it's wrong here
More training data does not stop the network memorising the existing training set, so the validation loss still rises. It is tempting because additional examples reduce overfitting when the dataset is small, and would be correct if the gap stemmed from insufficient representative fraud samples rather than prolonged training.
- ✗
Increase the learning rate
Why it's wrong here
Raising the learning rate enlarges weight updates, which accelerates divergence rather than halting it, so validation loss keeps climbing. It is tempting because a higher rate speeds convergence when training loss plateaus early, and would be correct if both losses were still falling but progressing too slowly.
Go deeper
Related to this question
About these practice questions
Courseiva writes every AIF-C01 question from scratch — 862 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.