- A
Export to Amazon S3 as a CSV file.
Why wrong: Exporting to S3 requires a separate ingestion job to load data into Feature Store.
- B
Export to a Jupyter notebook for further processing.
Why wrong: Exporting to a notebook requires manual steps to create and ingest the feature group.
- C
Export to Amazon Redshift for analysis.
Why wrong: Exporting to Redshift is meant for BI, not directly reusable for ML training in SageMaker.
- D
Export to a feature group in SageMaker Feature Store.
Data Wrangler can directly write to a feature group, making the features immediately available for training and inference via the Feature Store.
Question 104 of 1,000
mediumMultiple ChoiceObjective-mapped
Exporting Data from SageMaker Data Wrangler to Feature Store
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 S3 requires a separate ingestion job to load data into Feature Store.
- ✗
Export to a Jupyter notebook for further processing.
Why it's wrong here
Exporting to a notebook requires manual steps to create and ingest the feature group.
- ✗
Export to Amazon Redshift for analysis.
Why it's wrong here
Exporting to Redshift is meant for BI, not directly reusable for ML training in SageMaker.
- ✓
Export to a feature group in SageMaker Feature Store.
Why this is correct
Data Wrangler can directly write to a feature group, making the features immediately available for training and inference via the Feature Store.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
When you export to a feature group, Data Wrangler automatically maps the transformed columns to the feature group's schema, handles record identifiers and event times, and writes data to both the online store (for real-time inference) and offline store (for training) in a single operation. Under the hood, it uses the SageMaker Feature Store `IngestionManager` to batch-put records, ensuring consistency and avoiding duplicate writes by leveraging the feature group's `RecordIdentifierFeatureName` and `EventTimeFeatureName`. A real-world scenario is a fraud detection pipeline where features like transaction velocity must be available in milliseconds for inference; exporting directly to a feature group eliminates the latency of intermediate storage and manual ingestion.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Export to a feature group in SageMaker Feature Store. — Option D is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer uses Amazon SageMaker Data Wrangler to preprocess a dataset. After applying a transform, the engineer wants to export the data to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should they choose?
medium- A.Export to Amazon DynamoDB
- ✓ B.Export to Amazon SageMaker Feature Store
- C.Export to Amazon S3 as CSV
- D.Export to Amazon SageMaker Pipelines
Why B: SageMaker Data Wrangler can export directly to a feature group in Feature Store, making the features available for both training (offline) and inference (online).
Last reviewed: Jul 4, 2026
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.
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