MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a dataset. The dataset contains a feature 'age' with values ranging from 0 to 120. The data scientist wants to detect outliers. Which built-in transform in Data Wrangler is most appropriate for this task?
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
The 'Handle Outliers' transform in Amazon SageMaker Data Wrangler provides built-in methods such as IQR (Interquartile Range) and z-score to detect and handle outliers in numeric features like 'age'. Option B (Scale and Normalize) is used to rescale features but does not detect outliers. Option C (Handle Missing) deals with missing values, not outliers. Option D (Encode Categorical) is for converting categorical variables to numerical, not for outlier detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Handle Outliers
Why this is correct
This transform includes outlier detection methods such as IQR and z-score.
- ✗
Scale and Normalize
Why it's wrong here
This transform standardizes or normalizes features but does not identify outliers.
- ✗
Handle Missing
Why it's wrong here
This transform addresses null values, not outliers.
- ✗
Encode Categorical
Why it's wrong here
This transform converts categorical variables to numeric, not for outlier detection.
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