- A
Increase the batch size
Why wrong: Larger batch sizes can stabilize training but are less effective against overfitting; they may even reduce generalization.
- B
Add more convolutional layers
Why wrong: Adding more layers increases model capacity, likely increasing overfitting further.
- C
Increase the learning rate
Why wrong: Increasing learning rate may cause divergence and does not address overfitting.
- D
Apply dropout regularization
Dropout prevents co-adaptation of neurons, acting as a regularizer to reduce overfitting.
Question 28 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning team is developing a deep…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 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?
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.
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 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 prevents co-adaptation of neurons, acting as a regularizer to reduce overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Dropout works by sampling a sub-network from the full network at each training step, effectively training an ensemble of models without the computational cost. During inference, dropout is disabled, and weights are scaled by the dropout rate (e.g., 0.5) to maintain expected output magnitude. In practice, dropout is often applied after fully connected layers or convolutional layers with high dropout rates (0.2–0.5) to combat overfitting in deep networks.
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
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 MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-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
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.
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