- A
Create a scheduled SageMaker Pipeline by directly converting the Data Wrangler flow
Why wrong: Data Wrangler does not natively export to Pipelines; you need to export to Processing job and then integrate.
- B
Convert the data flow directly to Amazon SageMaker Autopilot
Why wrong: Autopilot automates model building, not data preparation pipelines.
- C
Export the data flow to a SageMaker Processing job
Data Wrangler can generate a processing script that runs as a SageMaker Processing job.
- D
Deploy the data flow as a real-time inference endpoint
Why wrong: Data Wrangler is for batch preparation, not real-time inference.
- E
Export the data flow as a standalone Python script
Data Wrangler can generate a Python script that can be run independently.
Question 846 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 Practice Question: Using Amazon SageMaker Data Wrangler to prepare a…
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 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)
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
Option C is correct because 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.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
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 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.
Detailed technical explanation
How to think about this question
When you export a Data Wrangler flow to a SageMaker Processing job, it generates a Python script that uses the SageMaker SDK to run the transformations on a specified dataset. Under the hood, this creates a ProcessingJob resource that spins up a managed container with the script, allowing you to parameterize inputs, outputs, and instance types. In real-world scenarios, this is often combined with SageMaker Pipelines to trigger the job on a schedule or in response to new data arriving in S3.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 the data flow to a SageMaker Processing job — Option C is correct because 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.
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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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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