MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited 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
✓
Amazon SageMaker Processing
Amazon SageMaker Processing allows you to run custom data processing scripts (e.g., using pandas) that can handle grouping rare categories into 'Other' based on frequency thresholds. Option B (Data Wrangler) is a visual tool that may not offer the same level of customization for complex grouping logic. Option C (AWS Glue Studio) is a visual ETL tool but lacks tight integration with SageMaker and may be less efficient for this specific task. Option D (Autopilot) is designed for automated model building, not custom data processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker Processing
Why this is correct
Processing jobs allow custom scripts for flexible data transformation.
- ✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Data Wrangler is visual but may not support complex custom grouping logic.
- ✗
AWS Glue Studio
Why it's wrong here
Glue Studio is visual ETL but not as integrated with SageMaker.
- ✗
Amazon SageMaker Autopilot
Why it's wrong here
Autopilot is for automated ML, not custom data processing.
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