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 data scientist is training a neural network for a multi-class classification problem. The model is overfitting. Which TWO of the following techniques can help reduce overfitting? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add dropout layers after the hidden layers.
Option B is correct because dropout layers randomly deactivate a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces co-adaptation among neurons and is a standard regularization technique to combat overfitting in neural networks.
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.
✗
Increase the number of hidden layers.
Why it's wrong here
Increasing layers increases model complexity and overfitting.
✓
Add dropout layers after the hidden layers.
Why this is correct
Dropout randomly drops units during training, reducing co-adaptation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the learning rate.
Why it's wrong here
Decreasing learning rate affects convergence speed, not overfitting directly.
✓
Add L2 regularization to the loss function.
Why this is correct
L2 regularization penalizes large weights and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the batch size.
Why it's wrong here
Reducing batch size introduces noise but is not a standard method to reduce overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between techniques that reduce overfitting (regularization) versus those that improve training dynamics (learning rate, batch size), leading candidates to mistakenly select options like decreasing the learning rate or reducing batch size as primary overfitting solutions.
Detailed technical explanation
How to think about this question
Dropout works by sampling a sub-network from the full network at each training step, effectively performing model averaging over an exponential number of architectures. L2 regularization (weight decay) adds a penalty proportional to the square of the weight magnitudes to the loss function, which shrinks weights toward zero and limits the model's capacity to fit noise. In practice, combining dropout with L2 regularization is a common strategy for deep networks, as they address overfitting through complementary mechanisms.
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 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.
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: Add dropout layers after the hidden layers. — Option B is correct because dropout layers randomly deactivate a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces co-adaptation among neurons and is a standard regularization technique to combat overfitting in neural networks.
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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