- A
Save the flow as a SageMaker Pipeline
Why wrong: Saving as a pipeline is for automation, not export.
- B
Export the flow to an S3 bucket
Why wrong: Exporting to S3 is possible but does not directly trigger a training job.
- C
Create a SageMaker training job directly from the flow
Data Wrangler can directly start a training job.
- D
Export the flow as a Lambda function
Why wrong: Data Wrangler does not export to Lambda.
- E
Create a new feature group in SageMaker Feature Store
Data Wrangler can create feature groups for online/offline storage.
Question 986 of 1,000
hardMultiple SelectObjective-mapped
Exporting Data Wrangler Flows to SageMaker Training Job and Feature Store
This MLA-C01 practice question tests your understanding of sagemaker data wrangler. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: sageMaker Data Wrangler. 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 data engineer is building a data preparation pipeline using Amazon SageMaker Data Wrangler. They need to export the transformed data for both batch training in SageMaker and real-time inference from a Feature Store. Which TWO actions should they take? (Choose TWO.)
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
✓
Create a SageMaker training job directly from the flow
The correct answers are C and E. Amazon SageMaker Data Wrangler can directly create a SageMaker training job from the flow (option C) for batch training, and it can export the transformed data to a new feature group in SageMaker Feature Store (option E) for real-time inference. Option A (saving the flow as a SageMaker Pipeline) is useful for reproducibility but does not directly export data for the specified purposes. Option B (exporting to an S3 bucket) is a data export but does not create a training job or feature group automatically. Option D (exporting as a Lambda function) is not a native Data Wrangler export capability; Data Wrangler does not directly export to Lambda for data preparation pipelines.
Key principle: SageMaker Data Wrangler
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Save the flow as a SageMaker Pipeline
Why it's wrong here
Saving as a pipeline is for automation, not export.
- ✗
Export the flow to an S3 bucket
Why it's wrong here
Exporting to S3 is possible but does not directly trigger a training job.
- ✓
Create a SageMaker training job directly from the flow
Why this is correct
Data Wrangler can directly start a training job.
Related concept
SageMaker Data Wrangler
- ✗
Export the flow as a Lambda function
Why it's wrong here
Data Wrangler does not export to Lambda.
- ✓
Create a new feature group in SageMaker Feature Store
Why this is correct
Data Wrangler can create feature groups for online/offline storage.
Related concept
SageMaker Data Wrangler
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- SageMaker Data Wrangler
- SageMaker Feature Store
- SageMaker Training Job
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
SageMaker Data Wrangler
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Review sageMaker Data Wrangler, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
SageMaker Data Wrangler
What is the correct answer to this question?
The correct answer is: Create a SageMaker training job directly from the flow — The correct answers are C and E. Amazon SageMaker Data Wrangler can directly create a SageMaker training job from the flow (option C) for batch training, and it can export the transformed data to a new feature group in SageMaker Feature Store (option E) for real-time inference. Option A (saving the flow as a SageMaker Pipeline) is useful for reproducibility but does not directly export data for the specified purposes. Option B (exporting to an S3 bucket) is a data export but does not create a training job or feature group automatically. Option D (exporting as a Lambda function) is not a native Data Wrangler export capability; Data Wrangler does not directly export to Lambda for data preparation pipelines.
What should I do if I get this MLA-C01 question wrong?
Review sageMaker Data Wrangler, then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
SageMaker Data Wrangler
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Last reviewed: Jul 4, 2026
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