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MLS-C01 Exploratory Data Analysis Practice Question

During exploratory data analysis on a dataset with 1 million rows, a data scientist notices that the distribution of the target variable is highly imbalanced (99% class A, 1% class B). Which technique should be applied to address this imbalance before model training?

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) generates synthetic samples for the minority class, balancing the dataset. Option A is wrong because random undersampling can discard important data. Option B is wrong because scaling does not address imbalance. Option C is wrong because PCA does not fix imbalance.

Answer analysis

Option-by-option breakdown

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

  • ✗

    Randomly undersample the majority class to match the minority class size

    Why it's wrong here

    Undersampling may lose valuable information from majority class.

  • ✗

    Apply standard scaling to all features

    Why it's wrong here

    Scaling does not affect class imbalance.

  • ✗

    Use PCA to reduce dimensionality and oversample in principal component space

    Why it's wrong here

    PCA does not directly address class imbalance.

  • ✓

    Use SMOTE to generate synthetic samples for the minority class

    Why this is correct

    SMOTE creates synthetic examples to balance classes.

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Same concept, more angles

2 more ways this is tested on MLS-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?

easy
  • A.Apply PCA to reduce dimensionality
  • B.Remove outliers from the majority class
  • C.Use cross-validation to evaluate the model
  • D.Apply feature scaling to all features
  • ✓ E.Use SMOTE to generate synthetic samples for the minority class

Why E: SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A (PCA) is wrong because dimensionality reduction does not address class imbalance. Option B (removing outliers) is wrong because it may worsen imbalance and is not a standard technique for imbalance. Option C (cross-validation) is a model evaluation technique, not a solution for imbalance. Option D (feature scaling) does not affect class distribution.

Variation 2. A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?

medium
  • A.Train the model on the raw data without any modification.
  • ✓ B.Apply SMOTE to generate synthetic samples for the minority class.
  • C.Use accuracy as the evaluation metric and train on the raw data.
  • D.Under-sample the majority class to match the minority class size.

Why B: SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which helps balance the dataset and improves model performance on the minority class without losing information from the majority class. Option A is wrong: training on raw data without addressing imbalance will cause the model to be biased toward the majority class and perform poorly on the minority class. Option C is wrong: accuracy is not a suitable evaluation metric for imbalanced datasets because a model that always predicts the majority class will achieve 99% accuracy, masking poor performance on the minority class; instead, metrics like precision, recall, F1-score, or AUC should be used. Option D is wrong: under-sampling the majority class to match the minority class size discards a large amount of data, potentially losing valuable patterns and reducing model performance.

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.