MLS-C01 Exploratory Data Analysis Practice Question
A company uses Amazon SageMaker Data Wrangler to perform exploratory data analysis. They want to detect outliers in a numerical column using the Interquartile Range (IQR) method. Which transformation should they apply in Data Wrangler?
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
✓
Handle outliers
Amazon SageMaker Data Wrangler provides a 'Handle outliers' transform that supports IQR-based outlier detection. Option A (Impute) is used to fill missing values, not detect outliers. Option B (Normalize) scales data to a standard range. Option D (Binning) groups continuous values into intervals. Therefore, the correct transform to apply for IQR outlier detection is 'Handle outliers'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute
Why it's wrong here
Imputation handles missing values.
- ✗
Normalize
Why it's wrong here
Normalization rescales, does not detect outliers.
- ✓
Handle outliers
Why this is correct
This transform supports IQR method.
- ✗
Binning
Why it's wrong here
Binning discretizes, not for outlier detection.
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