MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)
⚠ Common exam trap
AWS often tests the misconception that mode imputation (Option A) is a safe default for missing data, but it ignores feature relationships and can distort distributions, whereas multiple imputation is preferred for non-trivial missingness.
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 multiple imputation to fill in the missing values.
Multiple imputation is a robust statistical technique that accounts for uncertainty in missing values by creating multiple complete datasets, analyzing each, and pooling results. This is particularly appropriate for a dataset with 10,000 rows and 20 features, as it preserves the sample size and avoids bias that simpler methods might introduce.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace missing values with the mode of the feature.
Why it's wrong here
Mode imputation is simplistic and may introduce bias.
- ✗
Identify and remove outliers from the feature.
Why it's wrong here
Outlier removal does not address missing values.
- ✓
Use multiple imputation to fill in the missing values.
Why this is correct
Multiple imputation creates several plausible imputed datasets and combines results.
- ✓
Delete all rows that contain missing values for this feature.
Why this is correct
If missingness is random and 15% is acceptable, listwise deletion is straightforward.
- ✗
Drop the entire feature from the dataset.
Why it's wrong here
Dropping a feature with only 15% missing may discard valuable information.
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