Quick Answer
The answer is L2 regularization, dropout layers, and early stopping. These three strategies directly combat overfitting in deep neural networks by imposing constraints on the learning process: L2 regularization penalizes large weight values to prevent the model from fitting noise, dropout randomly deactivates neurons during training to force the network to learn redundant representations and avoid co-adaptation, and early stopping halts training once validation performance plateaus, preventing the model from memorizing training data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of regularization techniques versus model capacity adjustments—a common trap is confusing increasing model capacity or learning rate with overfitting reduction, when in fact those changes worsen the problem. To remember the three valid strategies, think of the mnemonic "LED": L2 regularization, Early stopping, and Dropout.
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which THREE of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)
Increase the number of layers.
Why wrong: More layers increase capacity and overfitting.
Use early stopping.
Early stopping prevents overfitting.
Increase the learning rate.
Why wrong: Higher learning rate does not reduce overfitting.
Add L2 regularization to the loss function.
L2 regularization reduces model complexity.
Use dropout layers.
Dropout is a regularization technique.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use early stopping.
Option A is correct because L2 regularization penalizes large weights. Option C is correct because dropout randomly drops units to prevent co-adaptation. Option E is correct because early stopping prevents overfitting. Option B is wrong because increasing model capacity increases overfitting. Option D is wrong because increasing learning rate may cause divergence.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the number of layers.
Why it's wrong here
More layers increase capacity and overfitting.
Use early stopping.
Why this is correct
Early stopping prevents overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Increase the learning rate.
Why it's wrong here
Higher learning rate does not reduce overfitting.
Add L2 regularization to the loss function.
Why this is correct
L2 regularization reduces model complexity.
Related concept
Read the scenario before looking for a memorised answer.
Use dropout layers.
Why this is correct
Dropout is a regularization technique.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use early stopping. — Option A is correct because L2 regularization penalizes large weights. Option C is correct because dropout randomly drops units to prevent co-adaptation. Option E is correct because early stopping prevents overfitting. Option B is wrong because increasing model capacity increases overfitting. Option D is wrong because increasing learning rate may cause divergence.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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