The answer is to apply L1 or L2 regularization. This is correct because the model is overfitting—it has memorized the training data with 50 features but fails to generalize to new data, a classic sign of high variance. L1 regularization (Lasso) and L2 regularization (Ridge) both penalize large coefficients, forcing the model to shrink or zero out less important features, which reduces complexity and improves test performance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of bias-variance tradeoff and regularization as a direct fix for overfitting without adding data. A common trap is to think adding more features or polynomial terms will help, but that actually worsens overfitting. Memory tip: think of Lasso as a “laser” that cuts coefficients to zero (L1), while Ridge “ridges” them down but never fully removes them (L2)—both combat overfitting by taming the model’s complexity.
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 machine learning engineer is training a linear regression model on a dataset with 50 features. After training, the model achieves high accuracy on the training set but poor accuracy on the test set. Which technique should the engineer use to address this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply L1 or L2 regularization
The model exhibits overfitting: high training accuracy but poor test accuracy. L1 (Lasso) or L2 (Ridge) regularization penalizes large coefficients, reducing model complexity and improving generalization. This directly addresses the variance problem without requiring more data or features.
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.
✗
Train a deeper neural network with more layers
Why it's wrong here
A more complex model is likely to overfit more.
✗
Add more features through feature engineering
Why it's wrong here
Adding features can increase overfitting.
✓
Apply L1 or L2 regularization
Why this is correct
Regularization penalizes large coefficients and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the size of the training dataset
Why it's wrong here
More data helps but is not always available and is not a direct regularization technique.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between overfitting and underfitting, and the trap here is that candidates may think adding more data (Option D) is the universal fix for overfitting, when in fact regularization is the most direct and efficient solution for a model with high variance.
Detailed technical explanation
How to think about this question
L1 regularization adds a penalty equal to the absolute value of the coefficients (λ∑|w|), which can drive some weights to zero, performing implicit feature selection. L2 regularization adds a penalty equal to the square of the coefficients (λ∑w²), which shrinks weights uniformly but never to zero. The regularization strength λ controls the bias-variance tradeoff; cross-validation is typically used to select the optimal λ.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Apply L1 or L2 regularization — The model exhibits overfitting: high training accuracy but poor test accuracy. L1 (Lasso) or L2 (Ridge) regularization penalizes large coefficients, reducing model complexity and improving generalization. This directly addresses the variance problem without requiring more data or features.
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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