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.
Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?
Apply L2 weight regularization
L2 regularization penalizes large weights.
Increase the number of hidden layers
Why wrong: More layers increase model complexity and overfitting.
Train for more epochs
Why wrong: More epochs can lead to overfitting.
Use data augmentation
Data augmentation increases effective training size.
Add dropout layers
Dropout reduces co-adaptation of neurons.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Apply L2 weight regularization
L2 weight regularization (also known as weight decay) penalizes large weights by adding a term to the loss function proportional to the sum of squared weights. This forces the network to learn simpler patterns and reduces sensitivity to noise in the training data, which is especially helpful when the dataset is small and prone to overfitting.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Apply L2 weight regularization
Why this is correct
L2 regularization penalizes large weights.
Related concept
Read the scenario before looking for a memorised answer.
Increase the number of hidden layers
Why it's wrong here
More layers increase model complexity and overfitting.
Train for more epochs
Why it's wrong here
More epochs can lead to overfitting.
Use data augmentation
Why this is correct
Data augmentation increases effective training size.
Related concept
Read the scenario before looking for a memorised answer.
Add dropout layers
Why this is correct
Dropout reduces co-adaptation of neurons.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the misconception that increasing model complexity (more layers or epochs) always improves performance, when in fact on small datasets it reliably worsens overfitting.
Detailed technical explanation
L2 regularization adds λ * Σ(w²) to the loss, where λ is a hyperparameter controlling the penalty strength; during backpropagation, this results in weight updates that shrink weights proportionally to their current magnitude. In practice, L2 regularization can be combined with early stopping to find a balance between underfitting and overfitting, and it is particularly effective when the input features are high-dimensional relative to the sample size.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Apply L2 weight regularization — L2 weight regularization (also known as weight decay) penalizes large weights by adding a term to the loss function proportional to the sum of squared weights. This forces the network to learn simpler patterns and reduces sensitivity to noise in the training data, which is especially helpful when the dataset is small and prone to overfitting.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
Last reviewed: Jun 11, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.