MLS-C01 Modeling Practice Question
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 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
✓
Add L2 regularization to the loss function.
Overfitting can be reduced by regularization techniques such as L2 regularization (Option A) which penalizes large weights, by dropout (Option E) which randomly ignores neurons during training, and by data augmentation (Option B) which increases the effective size of the training dataset by creating modified copies. Increasing model complexity by adding layers (Option C) would worsen overfitting, and reducing the learning rate (Option D) does not directly address overfitting—it affects convergence speed.
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.
- ✓
Use data augmentation to increase the training dataset size.
Why this is correct
Data augmentation creates more diverse training samples, reducing overfitting.
- ✗
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.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.