The answer is to use data augmentation to increase the training dataset size. This technique directly reduces overfitting by artificially expanding the dataset through transformations like rotations, flips, or noise injection, which forces the model to learn more robust, invariant features rather than memorizing noise or specific patterns in the limited original data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of regularization beyond traditional methods like L1/L2 or dropout—a common trap is to think only model complexity or weight penalties can fix overfitting, but data augmentation is a powerful alternative when those fail. Remember the memory tip: “Augment to augment generalization” — if your model is memorizing, make the data more diverse, not the model simpler.
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 company's machine learning model is overfitting to the training data. The data scientist has already tried reducing the model complexity and adding regularization, but the model still overfits. Which technique could the data scientist use to further reduce overfitting?
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 to increase the training dataset size
Data augmentation artificially increases the size and diversity of the training dataset by applying transformations (e.g., rotations, flips, noise injection) to existing samples. This exposes the model to more varied examples, reducing its tendency to memorize noise and improving generalization — directly countering overfitting when other methods have failed.
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 to increase the training dataset size
Why this is correct
Data augmentation creates more training examples, which helps the model generalize better and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the batch size
Why it's wrong here
Smaller batch sizes introduce noise but are not a primary method to reduce overfitting.
✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting if the model memorizes the training data.
✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate can destabilize training and may not reduce overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that hyperparameter tuning (e.g., batch size, learning rate) is a primary cure for overfitting, when in fact these parameters primarily affect optimization dynamics, not the fundamental data scarcity or memorization issue that data augmentation directly addresses.
Detailed technical explanation
How to think about this question
Under the hood, data augmentation works by expanding the empirical data distribution, effectively reducing the model's capacity to overfit to spurious correlations. For image data, common augmentations include random cropping, color jitter, and horizontal flips; for text, synonym replacement or back-translation. In production, augmentation must be applied consistently during training but not during inference, and care is needed to avoid unrealistic transformations that could misrepresent the true data distribution.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 to increase the training dataset size — Data augmentation artificially increases the size and diversity of the training dataset by applying transformations (e.g., rotations, flips, noise injection) to existing samples. This exposes the model to more varied examples, reducing its tendency to memorize noise and improving generalization — directly countering overfitting when other methods have failed.
What should I do if I get this MLS-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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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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