easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A team is developing a model to predict customer…
A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target variable is binary with 15% churn rate. The team wants to use logistic regression. Which data preprocessing step is MOST important to ensure proper convergence?
⚠ Common exam trap
AWS often tests the misconception that class imbalance is the primary barrier to convergence, when in fact feature scaling is the fundamental requirement for optimization algorithms in logistic regression.
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
✓
Standardize the features to have zero mean and unit variance
Logistic regression uses gradient descent or similar optimization algorithms that rely on the scale of the features. When features have different units or magnitudes, the cost function becomes elongated, causing slow or unstable convergence. Standardizing to zero mean and unit variance ensures that all features contribute equally to the gradient updates, leading to faster and more reliable convergence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove correlated features to reduce multicollinearity
Why it's wrong here
Multicollinearity affects interpretability but not necessarily convergence of logistic regression.
- ✗
Impute missing values with the median
Why it's wrong here
Missing value imputation is important but not the most critical for convergence.
- ✗
Apply SMOTE to balance the classes
Why it's wrong here
SMOTE addresses class imbalance but does not affect convergence of logistic regression.
- ✓
Standardize the features to have zero mean and unit variance
Why this is correct
Standardization ensures gradient descent converges faster and avoids dominance by large-scale features.
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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.