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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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