Quick Answer
The answer is L2 regularization, dropout, and early stopping. These three techniques reduce overfitting in neural networks by constraining model complexity or introducing noise during training. L2 regularization adds a penalty proportional to the squared magnitude of weights to the loss function, which discourages large weights and forces the network to learn simpler, more generalizable patterns. Dropout randomly drops a fraction of neurons during each forward pass, preventing co-adaptation and forcing the network to learn redundant, robust features. Early stopping halts training when validation performance stops improving, directly limiting the model’s capacity to memorize noise. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of regularization strategies versus data augmentation or cross-validation, which are also valid but not listed here. A common trap is confusing L1 regularization (which encourages sparsity) with L2, or assuming batch normalization reduces overfitting—it actually speeds convergence. Memory tip: think “Drop, Stop, and Squared Penalty” to recall dropout, early stopping, and L2 regularization.
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.
Which THREE techniques can help reduce overfitting in a neural network? (Choose 3)
Dropout
Dropout randomly drops neurons, reducing overfitting.
Increasing the number of layers
Why wrong: More layers increase capacity and overfitting.
Using a larger learning rate
Why wrong: Larger learning rate may cause divergence.
Early stopping
Stops training when validation loss increases.
L2 regularization
L2 penalty reduces weights, preventing overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Dropout
Dropout is a regularization technique that randomly drops 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 overfitting by introducing noise that improves generalization.
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.
Dropout
Why this is correct
Dropout randomly drops neurons, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Increasing the number of layers
Why it's wrong here
More layers increase capacity and overfitting.
Using a larger learning rate
Why it's wrong here
Larger learning rate may cause divergence.
Early stopping
Why this is correct
Stops training when validation loss increases.
Related concept
Read the scenario before looking for a memorised answer.
L2 regularization
Why this is correct
L2 penalty reduces weights, preventing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the misconception that increasing model capacity (e.g., more layers) or adjusting the learning rate can reduce overfitting, when in fact these techniques either exacerbate overfitting or address convergence issues rather than regularization.
Detailed technical explanation
Dropout works by sampling a sub-network from the full network at each training step, effectively averaging many sparse models during inference. L2 regularization adds a penalty proportional to the square of the weights to the loss function, which shrinks weights toward zero and reduces model complexity. Early stopping monitors validation loss and halts training when it begins to increase, preventing the model from memorizing noise in the training data.
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: Dropout — Dropout is a regularization technique that randomly drops 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 overfitting by introducing noise that improves generalization.
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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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)
hardWhy A: Dropout randomly drops units during training, L2 regularization penalizes large weights, and early stopping halts training when validation error increases. Data augmentation can also help but is not listed. Batch normalization may help but primarily for training stability.
Variation 2. Which THREE techniques can help reduce overfitting in a neural network? (Select THREE.)
hardWhy B: Dropout is correct because it randomly deactivates a fraction of neurons during training, forcing the network to learn redundant representations and preventing co-adaptation of features. This reduces overfitting by acting as an ensemble method without increasing computational cost at inference time.
Last reviewed: Jun 24, 2026
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