- A
Amazon S3
Why wrong: Data Wrangler can write to S3 as an intermediate step, but it is not a direct destination in the export options; typically you export to Feature Store or a training job.
- B
Amazon Kinesis Data Streams
Why wrong: Data Wrangler is for batch processing, not streaming.
- C
Amazon SageMaker Feature Store
Feature Store can serve both online (real-time) and offline (historical) features.
- D
Amazon SageMaker training job
Data Wrangler can export the transformed data directly to a SageMaker training job.
- E
Amazon Athena
Why wrong: Athena is a query service, not a destination for Data Wrangler exports.
Question 174 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 SageMaker Data Wrangler destinations Practice Question
This MLA-C01 practice question tests your understanding of sagemaker data wrangler destinations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: sageMaker Data Wrangler destinations. 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.
An ML engineer is using Amazon SageMaker Data Wrangler to create a data preparation flow. They want to output the transformed data to two destinations: one for real-time inference and one for historical analysis. Which TWO destinations can they choose? (Select 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
✓
Amazon SageMaker Feature Store
Amazon SageMaker Data Wrangler can output transformed data to multiple destinations. Amazon SageMaker Feature Store stores features for real-time inference (Online Store) and historical analysis (Offline Store), fulfilling both requirements. Additionally, Data Wrangler can directly output to a SageMaker training job, which is a valid destination for preparing data for model training. Thus, two correct destinations are Feature Store and training job.
Key principle: SageMaker Data Wrangler destinations
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon S3
Why it's wrong here
Data Wrangler can write to S3 as an intermediate step, but it is not a direct destination in the export options; typically you export to Feature Store or a training job.
- ✗
Amazon Kinesis Data Streams
Why it's wrong here
Data Wrangler is for batch processing, not streaming.
- ✓
Amazon SageMaker Feature Store
Why this is correct
Feature Store can serve both online (real-time) and offline (historical) features.
Related concept
SageMaker Data Wrangler destinations
- ✓
Amazon SageMaker training job
Why this is correct
Data Wrangler can export the transformed data directly to a SageMaker training job.
Related concept
SageMaker Data Wrangler destinations
- ✗
Amazon Athena
Why it's wrong here
Athena is a query service, not a destination for Data Wrangler exports.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates might think that only one destination is needed because Feature Store covers both use cases, but the question explicitly asks for TWO destinations. They may overlook that a training job is also a valid destination.
Detailed technical explanation
How to think about this question
Under the hood, Data Wrangler uses Apache Spark to process data, and its integration with SageMaker Feature Store leverages the Feature Store's Ingestion API to write to the Online Store (backed by DynamoDB for low-latency inference) and the Offline Store (backed by an S3 bucket with Glue Data Catalog). This dual-write capability ensures feature consistency across real-time and batch workflows without manual duplication.
KKey Concepts to Remember
- SageMaker Data Wrangler destinations
- 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 destinations
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.
Review sageMaker Data Wrangler destinations, then practise related MLA-C01 questions on the same topic to reinforce the concept.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
SageMaker Data Wrangler destinations
What is the correct answer to this question?
The correct answer is: Amazon SageMaker Feature Store — Amazon SageMaker Data Wrangler can output transformed data to multiple destinations. Amazon SageMaker Feature Store stores features for real-time inference (Online Store) and historical analysis (Offline Store), fulfilling both requirements. Additionally, Data Wrangler can directly output to a SageMaker training job, which is a valid destination for preparing data for model training. Thus, two correct destinations are Feature Store and training job.
What should I do if I get this MLA-C01 question wrong?
Review sageMaker Data Wrangler destinations, 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 destinations
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.