MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is using SageMaker Data Wrangler to prepare features for a classification model. Which TWO statements about feature engineering in Data Wrangler are correct?
⚠ Common exam trap
Candidates often assume Data Wrangler supports custom PySpark transformations (Option B) because it integrates with Spark, but in reality, custom code must be written outside the visual interface, and only built-in transforms are available within Data Wrangler itself.
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
✓
Transformations created in Data Wrangler can be exported as a SageMaker Processing script
SageMaker Data Wrangler allows you to export the entire data flow, including all transformations, as a SageMaker Processing script. This script can be run at scale on managed infrastructure, enabling you to operationalize the feature engineering pipeline for training or inference without manual rework.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data Wrangler only supports CSV and Parquet input formats
Why it's wrong here
Data Wrangler supports CSV, Parquet, JSON, and more.
- ✗
Data Wrangler enables writing custom PySpark transformations
Why it's wrong here
Data Wrangler uses its own transforms, not custom PySpark.
- ✓
Transformations created in Data Wrangler can be exported as a SageMaker Processing script
Why this is correct
Data Wrangler can generate a processing script for reuse.
- ✗
Data Wrangler automatically scales features for XGBoost models
Why it's wrong here
Scaling is not automatic and not required for tree-based models.
- ✓
Data Wrangler can export features to SageMaker Feature Store
Why this is correct
Features can be written directly to Feature Store.
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