The answer is to use dropout layers, L2 regularization, and data augmentation. These three actions directly combat overfitting by preventing the model from memorizing noise in the training data. Dropout randomly deactivates neurons during training, forcing the network to learn more robust features; L2 regularization penalizes large weights, smoothing the decision boundary; and data augmentation artificially expands the training set, reducing the model’s reliance on spurious patterns. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of regularization techniques in the context of deep learning on SageMaker—a common trap is confusing increased model complexity (more layers) with a solution, when in fact it worsens overfitting. Remember the mnemonic “DAD” for Dropout, Augmentation, and L2 (a form of weight Decay) to quickly recall the three effective actions.
MLS-C01 Modeling Practice Question
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.
A data scientist is building a deep learning model using Amazon SageMaker. The model is overfitting the training data. Which THREE actions can help reduce overfitting?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add L2 regularization to the loss function.
Overfitting can be reduced by regularization (L2), dropout, data augmentation (increases effective data size), early stopping, and reducing model complexity. Increasing model complexity (more layers) would increase overfitting (Option B). So correct: A, C, D.
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.
✓
Add L2 regularization to the loss function.
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use data augmentation to increase the training dataset size.
Why this is correct
Data augmentation creates more diverse training samples, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of layers in the network.
Why it's wrong here
Increasing layers increases model complexity and likely overfitting.
✗
Reduce the learning rate.
Why it's wrong here
Reducing learning rate slows training but does not directly reduce overfitting; it may help with convergence but not a primary method.
✓
Use dropout layers in the network.
Why this is correct
Dropout randomly drops neurons during training, preventing co-adaptation and reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add L2 regularization to the loss function. — Overfitting can be reduced by regularization (L2), dropout, data augmentation (increases effective data size), early stopping, and reducing model complexity. Increasing model complexity (more layers) would increase overfitting (Option B). So correct: A, C, D.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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