hardMultiple Choice
MLA-C01 Practice Question: A machine learning team is building a model to…
A machine learning team is building a model to predict customer churn. The dataset includes a feature 'customer_tenure' with values ranging from 1 to 100 months, and 'monthly_spend' ranging from $10 to $5000. The model will use gradient boosting. Which feature scaling approach is most appropriate?
⚠ Common exam trap
AWS often tests the misconception that all machine learning models require feature scaling, but the trap here is that tree-based ensemble methods like gradient boosting are scale-invariant, so candidates incorrectly apply scaling techniques that are only necessary for distance-based or gradient-based algorithms.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
No scaling is required for gradient boosting
Gradient boosting, as a tree-based ensemble method, makes split decisions based on feature values rather than distances or gradients that depend on feature magnitudes. Therefore, it is inherently scale-invariant, and no feature scaling is required. Applying scaling like MinMax or StandardScaler would not improve model performance and could introduce unnecessary computational overhead.
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 log transformation to 'monthly_spend' only
Why it's wrong here
Log transformation may help with skewness but is not required for tree models.
- ✗
Apply MinMaxScaler to both features
Why it's wrong here
Not necessary for tree-based models, but acceptable.
- ✓
No scaling is required for gradient boosting
Why this is correct
Tree-based models do not require feature scaling.
- ✗
Apply StandardScaler to both features
Why it's wrong here
Not necessary; tree-based models are invariant to monotonic transformations.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.