The answer is applying L2 regularization and stratified k-fold cross-validation. L2 regularization works by adding a penalty proportional to the square of the model weights, which shrinks less important features toward zero and reduces overfitting to noise in the training data, directly improving model generalization to new product categories. Stratified k-fold cross-validation ensures each fold preserves the original class distribution, providing a more robust evaluation across diverse data subsets and preventing the model from memorizing patterns unique to a single train-test split. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of variance reduction techniques versus data augmentation or feature engineering—a common trap is choosing more data or simpler models, but regularization and cross-validation directly combat overfitting. Remember the mnemonic “Regularize to Shrink, Stratify to Stabilize” to recall that L2 shrinks weights while stratified folds stabilize validation across varied data.
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 company is building a sentiment analysis model for customer reviews. The dataset is balanced with 10,000 positive and 10,000 negative reviews. The model achieves 95% accuracy on the test set but fails to generalize to new reviews from a different product category. Which TWO techniques can improve generalization?
Increase the training dataset size by collecting more reviews
Why wrong: More data helps, but the question asks for techniques that can be applied to the existing scenario; collecting more data may not be immediately possible.
B
Use stratified k-fold cross-validation during training
Cross-validation provides a more reliable estimate of generalization and helps tune hyperparameters.
C
Apply L2 regularization to the model
Regularization penalizes large weights and reduces overfitting.
D
Add more features like review length and word count
Why wrong: Adding more features may lead to overfitting if not carefully engineered.
E
Use a more complex model with more layers
Why wrong: More complexity increases the risk of overfitting.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use stratified k-fold cross-validation during training
Option B is correct because stratified k-fold cross-validation ensures that each fold maintains the same class distribution as the original dataset, which helps the model learn more robust patterns across different subsets of data. This technique reduces variance in the evaluation and improves generalization to unseen data from different product categories by preventing overfitting to idiosyncrasies of a single train-test split.
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 training dataset size by collecting more reviews
Why it's wrong here
More data helps, but the question asks for techniques that can be applied to the existing scenario; collecting more data may not be immediately possible.
✓
Use stratified k-fold cross-validation during training
Why this is correct
Cross-validation provides a more reliable estimate of generalization and helps tune hyperparameters.
Related concept
Read the scenario before looking for a memorised answer.
✓
Apply L2 regularization to the model
Why this is correct
Regularization penalizes large weights and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add more features like review length and word count
Why it's wrong here
Adding more features may lead to overfitting if not carefully engineered.
✗
Use a more complex model with more layers
Why it's wrong here
More complexity increases the risk of overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing data size or model complexity always improves generalization, but the question specifically tests the understanding that cross-validation techniques like stratified k-fold directly address overfitting and domain shift by providing a more reliable estimate of model performance across diverse data splits.
Trap categories for this question
Scenario analysis trap
More data helps, but the question asks for techniques that can be applied to the existing scenario; collecting more data may not be immediately possible.
Detailed technical explanation
How to think about this question
Stratified k-fold cross-validation works by partitioning the data into k folds while preserving the original class ratio in each fold, ensuring that every fold is representative of the overall distribution. This technique is particularly effective when the dataset is balanced, as it prevents variance in evaluation metrics due to uneven class splits and helps detect overfitting early by testing the model on multiple distinct subsets. In real-world scenarios, such as sentiment analysis across product categories, cross-validation can reveal performance drops on specific folds that correspond to domain-specific language patterns, guiding further data collection or domain adaptation.
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: Use stratified k-fold cross-validation during training — Option B is correct because stratified k-fold cross-validation ensures that each fold maintains the same class distribution as the original dataset, which helps the model learn more robust patterns across different subsets of data. This technique reduces variance in the evaluation and improves generalization to unseen data from different product categories by preventing overfitting to idiosyncrasies of a single train-test split.
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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