MLS-C01 Modeling Practice Question

A company is using Amazon SageMaker to build a binary classification model for customer churn. The dataset is highly imbalanced (90% no churn, 10% churn). Which technique is MOST effective for handling class imbalance?

⚠ Common exam trap

It's easy for candidates to assume switching to a tree-based model (like random forest) inherently solves class imbalance, but the exam tests that explicit resampling or cost-sensitive techniques are required for effective handling.

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

✓

Use SMOTE to generate synthetic samples for the minority class.

SMOTE (Synthetic Minority Oversampling Technique) is the most effective option because it generates synthetic samples for the minority class by interpolating between existing minority instances, thereby balancing the dataset without discarding valuable majority-class data. This approach directly addresses the class imbalance in a binary classification task on SageMaker, improving model recall for the churn class without the information loss caused by undersampling.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use accuracy as the evaluation metric.
Why it's wrong here
Accuracy is not suitable for imbalanced datasets.
✗
Undersample the majority class.
Why it's wrong here
Undersampling may discard valuable data.
✓
Use SMOTE to generate synthetic samples for the minority class.
Why this is correct
SMOTE is a standard oversampling technique.
✗
Train a random forest model instead of logistic regression.
Why it's wrong here
Algorithm change does not address imbalance.

Go deeper

MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

Related to this question