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MLA-C01 Practice Question: A machine learning team is developing a deep…
A machine learning team is developing a deep learning model for image classification. They observe that the training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which strategy should they implement to address this issue?
⚠ Common exam trap
The AWS ML Engineer Associate exam often tests the distinction between underfitting and overfitting. The trap here is that candidates may confuse a rapidly decreasing training loss with successful learning, overlooking the validation loss divergence as the hallmark of overfitting that requires regularization rather than increased capacity or learning rate adjustments.
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 dropout regularization
The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Dropout regularization randomly deactivates a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features, thereby reducing the validation loss increase.
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 batch size
Why it's wrong here
Larger batch sizes can stabilize training but are less effective against overfitting; they may even reduce generalization.
- ✗
Add more convolutional layers
Why it's wrong here
Adding more layers increases model capacity, likely increasing overfitting further.
- ✗
Increase the learning rate
Why it's wrong here
Increasing learning rate may cause divergence and does not address overfitting.
- ✓
Apply dropout regularization
Why this is correct
Dropout randomly deactivates neurons during each training pass, which directly counteracts the overfitting causing validation loss to rise while training loss falls. By forcing the network to learn redundant, generalisable features rather than memorising the training images, it restores the validation-loss trajectory the stem requires.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.