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MLA-C01 Practice Question: A data scientist is using Amazon SageMaker Data…
A data scientist is using Amazon SageMaker Data Wrangler to create a data preparation flow. After completing the flow, the scientist wants to export the processed data to a feature group in Amazon SageMaker Feature Store for reuse in multiple training jobs. Which export option should the scientist choose in Data Wrangler?
⚠ Common exam trap
MLA-C01 often tests whether candidates confuse generic S3 export with the purpose-built Feature Store export — the key differentiator is that only the Feature Store export registers features for cross-job reuse with online/offline serving.
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 to Feature Store
Data Wrangler provides a direct 'Export to Feature Store' option that writes the transformed dataset into a SageMaker Feature Store feature group, making it immediately available for reuse across multiple training jobs and real-time inference. This is the purpose-built integration for feature reuse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export to S3
Why it's wrong here
Exporting to S3 writes transformed data as objects; it creates no feature group, so Feature Store cannot serve the features to training jobs. It is tempting because S3 is Data Wrangler's default destination for offline training datasets.
- ✗
Export as a Python script
Why it's wrong here
A Python script reproduces the transformation logic outside Data Wrangler but does not register a feature group or ingest records into the online and offline stores. It is tempting when the flow must run in custom code or pipelines rather than through managed Feature Store ingestion.
- ✗
Save as a Jupyter notebook
Why it's wrong here
A Jupyter notebook captures the flow's transformation code for interactive editing, yet it neither creates a feature group nor writes features into Feature Store. It is tempting for iterating on preprocessing logic before committing it to a repeatable pipeline.
- ✓
Export to Feature Store
Why this is correct
Export to Feature Store writes the processed flow output directly into a SageMaker Feature Store feature group, registering the features for reuse across multiple training jobs. This satisfies the requirement to make the prepared data reusable rather than exporting a one-off dataset.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.