MLS-C01 Exploratory Data Analysis Practice Question
A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset and notices that the target variable is highly imbalanced. Which technique should the data scientist apply to balance the dataset before training?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that random undersampling is always safe, but the trap here is that candidates may overlook the information loss from discarding majority class data, while SMOTE provides a more robust synthetic oversampling approach.
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
✓
Synthetic Minority Oversampling Technique (SMOTE)
Synthetic Minority Oversampling Technique (SMOTE) is the correct technique because it generates synthetic samples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, effectively balancing the dataset without simply duplicating data. Amazon SageMaker Data Wrangler includes a built-in SMOTE transform, making it directly applicable for handling imbalanced target variables during exploratory data analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Synthetic Minority Oversampling Technique (SMOTE)
Why this is correct
SMOTE creates synthetic minority samples to balance the dataset.
- ✗
One-hot encoding of the target variable
Why it's wrong here
Encoding the target is not applicable for balancing.
- ✗
Random undersampling of the majority class
Why it's wrong here
Undersampling loses data and may discard useful information.
- ✗
Min-Max scaling of all features
Why it's wrong here
Scaling does not address class imbalance.
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Written by Johnson Ajibi, MSc IT Security
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This MLS-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 MLS-C01 exam.