Question 241 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is performing exploratory data analysis on a dataset with 1 million rows and 50 features. The engineer wants to identify missing values and outliers. Which THREE approaches should the engineer use? (Choose three.)
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 a DataFrame.info() method to see non-null counts
Options B, D, and E are correct because they directly help identify missing values and outliers. DataFrame.info() shows non-null counts per column, revealing missing values. The missingno matrix visualizes missing data patterns across rows and columns. DataFrame.describe() provides summary statistics (count, mean, std, min, max, quartiles) that can indicate outliers (e.g., values far outside the interquartile range). Option A (correlation heatmap) is used to assess relationships between features, not to detect missing values or outliers. Option C (box plots for all features simultaneously) is impractical with 50 features because it would be cluttered and hard to interpret; box plots are more useful when applied selectively to a few features or after filtering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a correlation heatmap of all features
Why it's wrong here
Correlation heatmap does not show missing values or outliers.
- ✓
Use a DataFrame.info() method to see non-null counts
Why this is correct
info() shows non-null counts and data types.
- ✗
Plot box plots for all features simultaneously
Why it's wrong here
Box plots are impractical for 50 features on one plot.
- ✓
Use a missingno matrix to visualize missing data patterns
Why this is correct
Missingno matrix shows missingness patterns visually.
- ✓
Use a DataFrame.describe() to view summary statistics
Why this is correct
describe() gives count, mean, std, min, quartiles, max.
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Last reviewed: Jun 20, 2026
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