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 deep learning model for image classification. The model is overfitting on the training data. Which combination of techniques will most effectively reduce overfitting?
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 and use data augmentation
Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and prevents co-adaptation. Data augmentation artificially expands the training dataset by applying transformations (e.g., rotation, flipping, cropping), which reduces the model's ability to memorize spurious patterns and improves generalization. Together, these techniques directly counteract overfitting by increasing regularization and effective training diversity.
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.
✓
Add dropout layers and use data augmentation
Why this is correct
Dropout randomly drops units to prevent co-adaptation; data augmentation increases effective training set size, both reduce 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 primary method to reduce overfitting.
✗
Train for more epochs without early stopping
Why it's wrong here
More training without regularization increases overfitting.
✗
Increase the number of layers and neurons
Why it's wrong here
Increasing model complexity exacerbates overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing model complexity (more layers/neurons) or training longer will fix overfitting, when in reality these actions worsen it, and that simple hyperparameter changes like batch size reduction are not primary regularization techniques.
Detailed technical explanation
How to think about this question
Dropout works by sampling a subnetwork at each training step, effectively averaging many sparse models during inference, which is a form of Monte Carlo model averaging. Data augmentation leverages domain-specific invariances (e.g., translation invariance in images) to generate plausible new samples, and in modern frameworks like TensorFlow, it can be applied as a preprocessing layer that runs on the GPU. A subtle behavior is that dropout should be tuned (typical rates 0.2–0.5) and is often used with inverted scaling to maintain expected activation magnitudes during training.
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 and use data augmentation — Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and prevents co-adaptation. Data augmentation artificially expands the training dataset by applying transformations (e.g., rotation, flipping, cropping), which reduces the model's ability to memorize spurious patterns and improves generalization. Together, these techniques directly counteract overfitting by increasing regularization and effective training diversity.
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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