- A
Increase the number of layers.
Why wrong: More layers increase capacity and overfitting.
- B
Use early stopping.
Early stopping prevents overfitting.
- C
Increase the learning rate.
Why wrong: Higher learning rate does not reduce overfitting.
- D
Add L2 regularization to the loss function.
L2 regularization reduces model complexity.
- E
Use dropout layers.
Dropout is a regularization technique.
Question 1,739 of 1,755
MLS-C01 Early stopping 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. A key principle to apply: early stopping. 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 of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)
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 early stopping.
Option B is correct because early stopping halts training when validation performance degrades, preventing overfitting. Option D is correct because L2 regularization adds a penalty on large weights, discouraging complexity. Option E is correct because dropout randomly drops neurons during training, reducing co-adaptation. Option A is wrong because adding more layers increases model capacity, which exacerbates overfitting. Option C is wrong because a higher learning rate can cause the loss to diverge and does not directly address overfitting.
Key principle: Early stopping
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of layers.
Why it's wrong here
More layers increase capacity and overfitting.
- ✓
Use early stopping.
Why this is correct
Early stopping prevents overfitting.
Related concept
Early stopping
- ✗
Increase the learning rate.
Why it's wrong here
Higher learning rate does not reduce overfitting.
- ✓
Add L2 regularization to the loss function.
Why this is correct
L2 regularization reduces model complexity.
Related concept
Early stopping
- ✓
Use dropout layers.
Why this is correct
Dropout is a regularization technique.
Related concept
Early stopping
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Early stopping
- L2 regularization
- Dropout
- Overfitting
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
Early stopping
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Review early stopping, then practise related MLS-C01 questions on the same topic to reinforce the concept.
- →
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
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Early stopping.
What is the correct answer to this question?
The correct answer is: Use early stopping. — Option B is correct because early stopping halts training when validation performance degrades, preventing overfitting. Option D is correct because L2 regularization adds a penalty on large weights, discouraging complexity. Option E is correct because dropout randomly drops neurons during training, reducing co-adaptation. Option A is wrong because adding more layers increases model capacity, which exacerbates overfitting. Option C is wrong because a higher learning rate can cause the loss to diverge and does not directly address overfitting.
What should I do if I get this MLS-C01 question wrong?
Review early stopping, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Early stopping
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
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
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.
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.