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MLA-C01 Practice Question: A machine learning engineer is using Amazon…
A machine learning engineer is using Amazon SageMaker Data Wrangler to create a data preparation pipeline. The pipeline includes multiple transforms such as handling missing values, scaling, and encoding. The engineer wants to export the prepared data directly to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should the engineer choose?
⚠ Common exam trap
Many candidates assume any export to Amazon S3 (Option A) is sufficient for reuse, but the question specifically requires export to a feature group, which is a distinct SageMaker Feature Store construct with its own schema, online/offline stores, and ingestion API—not just a file in S3.
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 a feature group in SageMaker Feature Store.
Amazon SageMaker Data Wrangler provides a built-in export destination for SageMaker Feature Store, allowing you to directly write the transformed data to a feature group without additional code. This enables seamless reuse of the prepared features for both training and real-time inference, leveraging the Feature Store's low-latency retrieval and versioning capabilities.
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 Amazon S3 as a CSV file.
Why it's wrong here
Exporting to Amazon S3 as CSV lands a flat file in a bucket, which creates no feature group and provides no online store for inference-time lookups. S3 export suits downstream batch processing or training jobs that read files directly.
- ✗
Export to a Jupyter notebook for further processing.
Why it's wrong here
Exporting to a Jupyter notebook only regenerates the transformation code for further manual processing; it writes no feature group, so features are not registered for reuse in training and inference. Notebook export suits iterating on custom code before building a pipeline.
- ✗
Export to Amazon Redshift for analysis.
Why it's wrong here
Exporting to Amazon Redshift produces a warehouse table for SQL analytics, not a SageMaker Feature Store feature group, so the prepared features cannot be ingested for training and inference. Redshift export suits downstream BI reporting or analytical querying, not serving engineered features to models.
- ✓
Export to a feature group in SageMaker Feature Store.
Why this is correct
Exporting to a feature group writes the transformed dataset straight into SageMaker Feature Store, satisfying the requirement to reuse prepared features across both training and inference. Data Wrangler's native feature group export handles schema and ingestion automatically, avoiding intermediate Amazon S3 staging and manual ingestion code that other export targets would demand.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.