- A
Use data augmentation
Data augmentation increases the diversity of training data without collecting new data, which helps reduce overfitting by making the model generalize better.
- B
Add dropout layers
Dropout layers randomly drop units during training, which prevents the model from relying too heavily on any single feature and acts as a regularization technique.
- C
Reduce the batch size
Why wrong: Reducing batch size introduces noise in the gradient estimates, which can sometimes help generalization but is not a primary or reliable method to mitigate overfitting.
- D
Increase the number of layers
Why wrong: Increasing the number of layers increases model capacity, which typically worsens overfitting rather than reducing it.
- E
Increase the number of training epochs
Why wrong: Increasing the number of training epochs can lead to overfitting if the model continues to learn noise in the training data, so it does not mitigate overfitting.
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.
An ML team trains a deep learning model using Amazon SageMaker with a custom Docker container. Training completes successfully, but the model's accuracy on the test set is significantly lower than expected. The team suspects overfitting. Which two actions should they take to mitigate overfitting? (Choose TWO.)
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
Use data augmentation
Dropout and data augmentation are effective regularization techniques to reduce overfitting. Option A (increasing epochs) would worsen overfitting. Option B (reducing batch size) can introduce noise but is not a primary regularization method. Option D (adding more layers) increases model capacity, likely worsening overfitting.
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.
- ✓
Use data augmentation
Why this is correct
Data augmentation increases the diversity of training data without collecting new data, which helps reduce overfitting by making the model generalize better.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Add dropout layers
Why this is correct
Dropout layers randomly drop units during training, which prevents the model from relying too heavily on any single feature and acts as a regularization technique.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the batch size
Why it's wrong here
Reducing batch size introduces noise in the gradient estimates, which can sometimes help generalization but is not a primary or reliable method to mitigate overfitting.
- ✗
Increase the number of layers
Why it's wrong here
Increasing the number of layers increases model capacity, which typically worsens overfitting rather than reducing it.
- ✗
Increase the number of training epochs
Why it's wrong here
Increasing the number of training epochs can lead to overfitting if the model continues to learn noise in the training data, so it does not mitigate overfitting.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Use data augmentation — Dropout and data augmentation are effective regularization techniques to reduce overfitting. Option A (increasing epochs) would worsen overfitting. Option B (reducing batch size) can introduce noise but is not a primary regularization method. Option D (adding more layers) increases model capacity, likely worsening overfitting.
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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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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