Quick Answer
The answer is to add early stopping based on validation loss and to implement dropout layers. Early stopping halts training when validation performance stops improving, directly countering the overfitting pattern where validation loss rises after an initial decrease. Dropout layers randomly deactivate a fraction of neurons during each forward pass, forcing the network to learn redundant, robust features rather than co-adapting to noise in the training data—a classic cause of overfitting in deep learning. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish between regularization techniques and optimization errors; a common trap is choosing data augmentation or reducing model complexity when the question specifically targets deep learning on SageMaker. Remember the mnemonic “Drop and Stop”—dropout for neuron co-adaptation, early stopping for validation loss divergence—to quickly recall the two actions that directly reduce overfitting without altering the dataset or architecture.
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 using Amazon SageMaker. The training loss is decreasing, but the validation loss starts increasing after 10 epochs. The model is overfitting. Which TWO actions should the data scientist take to reduce overfitting? (Choose 2.)
Increase the number of layers
Why wrong: Increases model capacity, worsens overfitting.
Remove L2 regularization
Why wrong: Regularization helps reduce overfitting.
Increase the number of training steps
Why wrong: More steps can lead to more overfitting.
Add dropout layers
Dropout regularizes by randomly dropping neurons.
Add early stopping based on validation loss
Stops training when validation loss stops improving.
Answer choices
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
Option D is correct because dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, a common cause of overfitting. This technique is particularly effective in deep learning models trained on SageMaker, where large architectures can quickly memorize training data.
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.
Increase the number of layers
Why it's wrong here
Increases model capacity, worsens overfitting.
Remove L2 regularization
Why it's wrong here
Regularization helps reduce overfitting.
Increase the number of training steps
Why it's wrong here
More steps can lead to more overfitting.
Add dropout layers
Why this is correct
Dropout regularizes by randomly dropping neurons.
Related concept
Read the scenario before looking for a memorised answer.
Add early stopping based on validation loss
Why this is correct
Stops training when validation loss stops improving.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse regularization techniques that reduce overfitting (dropout, L2, early stopping) with actions that increase model capacity (more layers, more steps), leading them to select options that would worsen the problem.
Detailed technical explanation
Dropout works by sampling a sub-network during each forward pass, effectively training an ensemble of models without the computational cost; the dropout rate (e.g., 0.5) controls the fraction of neurons dropped and must be tuned carefully. Early stopping monitors validation loss and halts training when it stops improving, often using a patience parameter to avoid stopping due to noise. In SageMaker, these techniques can be implemented via the framework's built-in callbacks (e.g., Keras EarlyStopping) or by customizing the training script.
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Add dropout layers — Option D is correct because dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, a common cause of overfitting. This technique is particularly effective in deep learning models trained on SageMaker, where large architectures can quickly memorize training data.
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.
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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.
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