- A
Apply log transformation to 'monthly_spend' only
Why wrong: Log transformation may help with skewness but is not required for tree models.
- B
Apply MinMaxScaler to both features
Why wrong: Not necessary for tree-based models, but acceptable.
- C
No scaling is required for gradient boosting
Tree-based models do not require feature scaling.
- D
Apply StandardScaler to both features
Why wrong: Not necessary; tree-based models are invariant to monotonic transformations.
Question 928 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning team is building a model to…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 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?
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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply StandardScaler to both features
Why it's wrong here
Not necessary; tree-based models are invariant to monotonic transformations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
Tree-based models like gradient boosting (e.g., XGBoost, LightGBM, CatBoost) learn decision rules by sorting feature values and finding optimal split points, which are unaffected by linear scaling or monotonic transformations. However, scaling can affect the interpretation of feature importance if features are on vastly different scales, but the model's predictive performance remains unchanged. In practice, scaling is only critical for models that use gradient descent (e.g., neural networks, linear regression) or distance-based metrics (e.g., k-NN, SVM).
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-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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Last reviewed: Jul 4, 2026
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.
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