MLS-C01 Exploratory Data Analysis Practice Question
Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between EDA actions (diagnostic) and preprocessing actions (transformative), so the trap here is that candidates confuse immediate imputation or scaling with proper exploratory steps, leading them to select B, C, or D instead of the correct diagnostic actions A and E.
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
✓
Calculate the percentage of missing values per column.
Calculating the percentage of missing values per column is a standard first step in exploratory data analysis (EDA) to quantify data completeness. This informs downstream decisions such as whether to impute, drop, or flag missing data, and helps assess the risk of bias or information loss. It is a diagnostic action, not a transformation, and should precede any imputation or removal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Calculate the percentage of missing values per column.
Why this is correct
Missing value counts inform imputation strategy.
- ✗
Normalize all features using Min-Max scaling.
Why it's wrong here
Normalization is a preprocessing step, not initial EDA.
- ✗
Remove all rows with outliers.
Why it's wrong here
Outliers may be valid; removal should be justified.
- ✗
Impute missing values with the mean immediately.
Why it's wrong here
Imputation should be decided after understanding the data.
- ✓
Visualize the distribution of each feature using histograms.
Why this is correct
Visualization reveals data shape and potential outliers.
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