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MLA-C01 Practice Question: A machine learning engineer needs to prepare a…
A machine learning engineer needs to prepare a dataset with a target variable that has severe class imbalance (1:1000). The dataset has 100,000 rows and 200 features. Which approach should the engineer use to address the class imbalance before training a classification model?
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 generates synthetic samples for the minority class, which is effective for severe imbalance. The other options either do not address imbalance (standardization) or are not appropriate for the scenario (undersampling alone discards too many majority samples, class weights are a modeling technique not a data preparation step).
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 SMOTE to generate synthetic samples for the minority class.
Why this is correct
SMOTE creates synthetic examples, balancing the classes without losing majority data.
- ✗
Apply random undersampling to the majority class to match the minority class count.
Why it's wrong here
Undersampling would discard 99.9% of data, losing valuable information.
- ✗
Set class weights inversely proportional to class frequencies in the model.
Why it's wrong here
Class weights are a model training technique, not a data preparation step.
- ✗
Use StandardScaler on the features to normalize them.
Why it's wrong here
StandardScaler normalizes features but does not address class imbalance.
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