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MLA-C01 Practice Question: Using Amazon SageMaker Data Wrangler to prepare a…
A company is using Amazon SageMaker Data Wrangler to prepare a dataset for training. They have created a data flow with multiple transforms. Which TWO actions can they take to operationalize the data preparation pipeline for production? (Choose 2)
⚠ Common exam trap
It's easy for candidates to confuse 'operationalizing for production' with real-time serving (Option D) or assume that Data Wrangler can directly feed into Autopilot (Option B), when in fact the correct approach is to export the flow to a batch processing job or standalone script.
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
✓
Export the data flow to a SageMaker Processing job
SageMaker Data Wrangler can export a data flow directly to a SageMaker Processing job. This allows the data preparation logic to be run as a managed, scalable batch job in production, integrating seamlessly with the SageMaker ecosystem for repeatable and scheduled 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.
- ✗
Create a scheduled SageMaker Pipeline by directly converting the Data Wrangler flow
Why it's wrong here
Data Wrangler does not natively export to Pipelines; you need to export to Processing job and then integrate.
- ✗
Convert the data flow directly to Amazon SageMaker Autopilot
Why it's wrong here
Autopilot automates model building, not data preparation pipelines.
- ✓
Export the data flow to a SageMaker Processing job
Why this is correct
Data Wrangler can generate a processing script that runs as a SageMaker Processing job.
- ✗
Deploy the data flow as a real-time inference endpoint
Why it's wrong here
Data Wrangler is for batch preparation, not real-time inference.
- ✓
Export the data flow as a standalone Python script
Why this is correct
Data Wrangler can generate a Python script that can be run independently.
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