MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset with mixed data types: numerical, categorical, and text. They want to use Amazon SageMaker Data Wrangler to create a quick visualization dashboard. Which set of transformations should they apply in Data Wrangler to handle all data types appropriately?
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 the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.
Amazon SageMaker Data Wrangler provides built-in analysis types that are appropriate for EDA with mixed data types: summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These allow quick visualization without complex transformations. Option B is incorrect because one-hot encoding and scatter matrix are not suitable for mixed types, and Data Wrangler does not offer a scatter matrix as a built-in analysis. Option C is incorrect because TF-IDF vectorization and k-means clustering are feature engineering and modeling steps, not EDA. Option D is incorrect because PCA is for dimensionality reduction and not a standard EDA visualization technique.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.
Why this is correct
These are appropriate EDA visualizations for different data types.
- ✗
Convert all features to numerical using one-hot encoding and then create a scatter matrix.
Why it's wrong here
One-hot encoding is a transformation; scatter matrix may not be suitable for high-cardinality categorical features.
- ✗
Apply TF-IDF vectorization to text and then run k-means clustering.
Why it's wrong here
TF-IDF and clustering are modeling steps.
- ✗
Use PCA to reduce dimensionality and then visualize the first two components.
Why it's wrong here
PCA is for dimensionality reduction, not initial EDA.
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