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 image classification. The dataset has 50,000 images across 100 classes. The model uses a ResNet-50 architecture pre-trained on ImageNet. The training loss decreases rapidly, but validation loss starts to increase after 5 epochs. Which of the following is the most effective technique to address this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use data augmentation to increase the diversity of the training set
The rapid decrease in training loss followed by an increase in validation loss after only 5 epochs is a classic sign of overfitting. Data augmentation artificially expands the training set by applying random transformations (e.g., rotations, flips, crops) to existing images, which improves the model's generalization and reduces overfitting. This is the most effective technique among the options because it directly addresses the lack of diverse training examples without changing the model architecture or training hyperparameters in a way that could destabilize learning.
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 learning rate
Why it's wrong here
Increasing learning rate may cause divergence and does not address overfitting.
✗
Add more layers to the network
Why it's wrong here
Adding layers increases model capacity and likely worsens overfitting.
✓
Use data augmentation to increase the diversity of the training set
Why this is correct
Data augmentation artificially expands the training set, reducing overfitting and improving generalization.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a smaller batch size
Why it's wrong here
Smaller batch sizes can introduce noise but are not the most effective for overfitting in this scenario.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse overfitting with underfitting or training instability, and incorrectly choose to increase learning rate or add layers, not recognizing that the validation loss rising while training loss falls is the textbook symptom of overfitting that requires regularization or more data.
Trap categories for this question
Scenario analysis trap
Smaller batch sizes can introduce noise but are not the most effective for overfitting in this scenario.
Detailed technical explanation
How to think about this question
Data augmentation works by generating on-the-fly variations of training images, effectively increasing the effective dataset size without collecting new data. For image classification, common augmentations include random horizontal flips, rotations, color jitter, and random cropping, which help the model learn invariant features. In practice, libraries like torchvision or TensorFlow's tf.image provide built-in augmentation pipelines that are applied per-batch during training, ensuring the model sees a different version of each image every epoch.
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: Use data augmentation to increase the diversity of the training set — The rapid decrease in training loss followed by an increase in validation loss after only 5 epochs is a classic sign of overfitting. Data augmentation artificially expands the training set by applying random transformations (e.g., rotations, flips, crops) to existing images, which improves the model's generalization and reduces overfitting. This is the most effective technique among the options because it directly addresses the lack of diverse training examples without changing the model architecture or training hyperparameters in a way that could destabilize learning.
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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Question Discussion
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