Question 1,529 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a text classification model using Amazon SageMaker. The dataset consists of 100,000 labeled documents. The data scientist notices that the model performs well on the training set but poorly on the validation set. Which regularization technique should the data scientist apply to reduce overfitting?
⚠ Common exam trap
Many exam-takers confuse batch normalization with regularization, but batch normalization primarily addresses internal covariate shift and training stability, not overfitting, while dropout is the explicit regularization technique for neural networks.
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
✓
Dropout
Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This directly addresses the overfitting symptom of high training accuracy and low validation accuracy by reducing the model's capacity to memorize noise in the training data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Dropout
Why this is correct
Dropout randomly drops units during training, preventing co-adaptation and reducing overfitting.
- ✗
Data augmentation
Why it's wrong here
Data augmentation is common for images, but for text it may not be straightforward.
- ✗
Batch normalization
Why it's wrong here
Batch normalization normalizes inputs, reducing internal covariate shift but not a strong regularizer.
- ✗
Early stopping
Why it's wrong here
Early stopping helps but is not the most direct regularization; dropout is more explicit.
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Last reviewed: Jun 24, 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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