MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 100 features and 10,000 samples. The target variable is highly imbalanced (1% positive class). Which exploratory data analysis step is most critical 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
✓
Plot the histogram of the target variable
The most critical EDA step for a highly imbalanced target variable is to examine its distribution. Therefore, plotting the histogram of the target variable (D) reveals the imbalance and guides decisions on resampling or evaluation metrics. Option A (PCA) is primarily for dimensionality reduction and not essential for understanding target balance. Option B (correlation matrix) examines feature relationships, not target distribution. Option C (mean imputation) addresses missing values, which is important but not the most critical for handling class 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.
- ✗
Apply PCA and visualize the first two principal components
Why it's wrong here
PCA is used for dimensionality reduction and visualization, but it does not directly reveal the degree of target imbalance. Therefore, it is not the most critical step for this issue.
- ✗
Compute pairwise correlation matrix among all features
Why it's wrong here
A pairwise correlation matrix examines relationships among features, not the distribution of the target variable. While useful for feature selection, it is not the most critical step for handling imbalance.
- ✗
Impute missing values using mean imputation
Why it's wrong here
Mean imputation addresses missing values, which is important for data quality but does not help understand or mitigate target imbalance. The most critical step is first to assess the imbalance itself.
- ✓
Plot the histogram of the target variable
Why this is correct
Plotting the histogram of the target variable directly shows the class distribution, confirming the severe imbalance (1% positive). This insight is crucial for deciding on resampling techniques, evaluation metrics, or algorithmic adjustments.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.