The answer is to apply L2 regularization, also known as Ridge regression, as it is the most effective approach to reduce overfitting in a linear regression model exhibiting high variance. This technique directly addresses the core problem of linear regression overfitting high variance L2 regularization by adding a penalty term proportional to the square of the model’s coefficients, which shrinks them toward zero without eliminating them entirely. This constraint reduces the model’s sensitivity to noise in the training data, thereby lowering test error and improving generalization. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to diagnose overfitting from a discrepancy between training and test RMSE and to select the appropriate regularization technique; a common trap is choosing L1 regularization (Lasso) which can zero out coefficients, but for reducing variance without sacrificing all features, L2 is preferred. Remember the memory tip: “Ridge shrinks, Lasso selects”—when you see high variance, think of Ridge to keep all features but dampen their impact.
MLS-C01 Modeling Practice Question
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.
An e-commerce company uses a linear regression model to predict customer lifetime value (LTV). The model shows high variance on the test set, with training RMSE much lower than test RMSE. Which of the following is the MOST effective approach 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
✓
Apply L2 regularization (Ridge regression)
High variance (low training RMSE, high test RMSE) indicates overfitting. L2 regularization (Ridge regression) adds a penalty proportional to the square of the coefficients, shrinking them toward zero without eliminating them, which reduces model complexity and improves generalization. This directly addresses overfitting by constraining the model's sensitivity to 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.
✓
Apply L2 regularization (Ridge regression)
Why this is correct
L2 regularization shrinks coefficients and reduces variance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a polynomial kernel in a support vector regressor
Why it's wrong here
Polynomial kernel can increase complexity and overfitting.
✗
Add more features, including interaction terms
Why it's wrong here
Adding features may increase variance.
✗
Increase training data size by duplicating existing samples
Why it's wrong here
Duplicating data does not add new information and may not reduce overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that adding more data always reduces overfitting, but the trap here is that duplicating existing samples (Option D) does not provide new, diverse examples and therefore fails to address the root cause of high variance.
Detailed technical explanation
How to think about this question
Under the hood, L2 regularization modifies the ordinary least squares objective function by adding λ * Σ(β_j²), where λ controls the regularization strength. This shrinks coefficients toward zero but never exactly to zero, preserving all features while reducing their influence. In real-world e-commerce LTV prediction, where features like purchase frequency and recency may be correlated, Ridge helps stabilize coefficient estimates and prevents the model from fitting to idiosyncratic patterns in the training data.
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 L2 regularization (Ridge regression) — High variance (low training RMSE, high test RMSE) indicates overfitting. L2 regularization (Ridge regression) adds a penalty proportional to the square of the coefficients, shrinking them toward zero without eliminating them, which reduces model complexity and improves generalization. This directly addresses overfitting by constraining the model's sensitivity to 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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Question Discussion
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