MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer is using SageMaker Data Wrangler to perform data validation. Which step should be added to the pipeline to ensure data quality before training?
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by choosing a custom Processing job or external service, missing that Data Wrangler's built-in 'Data Quality' transformation is the most direct and efficient way to validate data quality within the same pipeline.
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
✓
Apply a 'Data Quality' transformation in Data Wrangler to validate column statistics
SageMaker Data Wrangler includes a built-in 'Data Quality' transformation that allows you to validate column statistics (e.g., missing values, min/max, distinct counts) directly within the visual pipeline. This step ensures data quality without requiring custom code or external services, integrating seamlessly with the Data Wrangler workflow for pre-training validation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a custom SageMaker Processing job for validation
Why it's wrong here
Unnecessary when Data Wrangler already has validation.
- ✓
Apply a 'Data Quality' transformation in Data Wrangler to validate column statistics
Why this is correct
Data Wrangler provides built-in data quality checks.
- ✗
Use AWS Glue DataBrew to profile the dataset
Why it's wrong here
DataBrew is an alternative, not integrated in Data Wrangler.
- ✗
Add a SageMaker Pipeline step to check data quality after Data Wrangler
Why it's wrong here
Pipeline can orchestrate but Data Wrangler itself has validation.
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