MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset containing customer transaction records. The target variable is 'churn' (1 = churned, 0 = not churned). Which TWO actions should the scientist take to understand the data distribution and prepare for modeling?
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 frequency of the target variable to check for class imbalance.
Visualizing class imbalance and identifying missing values are fundamental EDA steps. Option A (PCA) is for dimensionality reduction, not initial EDA. Option B (gradient boosting) is modeling, not EDA. Option E (one-hot encoding) is for categorical variables, but not an EDA action. The correct actions are C and D.
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 Principal Component Analysis (PCA) to reduce dimensionality.
Why it's wrong here
PCA is a modeling step, not EDA.
- ✗
Train a gradient boosting model to identify important features.
Why it's wrong here
Model training comes after EDA.
- ✓
Plot the frequency of the target variable to check for class imbalance.
Why this is correct
Essential to detect imbalance.
- ✓
Check for missing values in each column and decide on an imputation strategy.
Why this is correct
Missing data analysis is critical in EDA.
- ✗
Convert categorical variables into one-hot encoded vectors.
Why it's wrong here
Feature engineering, not EDA.
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Same concept, more angles
1 more way 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. A data scientist is working with a dataset containing text reviews. The goal is to build a sentiment analysis model. Which EDA step is most critical before feature extraction?
hard- A.Calculating the vocabulary size
- B.Creating a word cloud
- C.Removing stop words
- ✓ D.Checking the distribution of sentiment labels
Why D: Checking the distribution of sentiment labels is critical before feature extraction because it reveals class imbalance, which can bias the model towards the majority class and affect evaluation metrics. This EDA step enables informed decisions about resampling or weighting techniques. Option A (vocabulary size) is not a critical first step; option B (word cloud) is a visualization tool, not essential; option C (removing stop words) is a preprocessing step, not part of EDA.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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