The answer is dropout regularization, which directly addresses the overfitting symptom of high training accuracy paired with poor validation accuracy. Dropout works by randomly dropping a fraction of neurons during each forward pass, forcing the network to learn redundant, robust representations rather than relying on any single feature—this prevents the model from memorizing noise in the training data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of regularization trade-offs; a common trap is confusing dropout with L1 or L2 regularization, which penalize weights instead of deactivating neurons. Remember that dropout is especially effective for large, deep networks where overfitting is likely, such as text classification models with many parameters. A simple memory tip: think of dropout as “forcing the network to work with a smaller, randomly chosen team each round,” so it never becomes too dependent on any one player.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 text classification model using Amazon SageMaker. The dataset consists of 100,000 labeled documents. The data scientist notices that the model performs well on the training set but poorly on the validation set. Which regularization technique should the data scientist apply to reduce overfitting?
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 model from relying too heavily on any single feature and forces it to learn more robust representations. This directly addresses the overfitting symptom of high training accuracy and low validation accuracy by reducing the model's capacity to memorize noise in the training data.
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.
✓
Dropout
Why this is correct
Dropout randomly drops units during training, preventing co-adaptation and reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Data augmentation
Why it's wrong here
Data augmentation is common for images, but for text it may not be straightforward.
✗
Batch normalization
Why it's wrong here
Batch normalization normalizes inputs, reducing internal covariate shift but not a strong regularizer.
✗
Early stopping
Why it's wrong here
Early stopping helps but is not the most direct regularization; dropout is more explicit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse batch normalization with regularization, but batch normalization primarily addresses internal covariate shift and training stability, not overfitting, while dropout is the explicit regularization technique for neural networks.
Detailed technical explanation
How to think about this question
Dropout works by randomly setting a fraction (e.g., 0.5) of neuron activations to zero during each forward pass, effectively training a thinned network and forcing the model to learn redundant representations. At inference time, dropout is disabled and the weights are scaled by the dropout rate to maintain expected output magnitude. In Amazon SageMaker, dropout is implemented as a layer in frameworks like TensorFlow or PyTorch, and the rate is a hyperparameter that must be tuned; too high a rate can cause underfitting.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Dropout — Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This directly addresses the overfitting symptom of high training accuracy and low validation accuracy by reducing the model's capacity to memorize noise in the training data.
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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