- A
Use AWS Step Functions to trigger an MLflow run and then call SageMaker CreateEndpoint
Why wrong: This is possible but less integrated than Pipelines; Step Functions would require custom glue code.
- B
Use SageMaker Pipelines with the MLflow integration to register the model and deploy via a Transform step
SageMaker Pipelines can automate the workflow: get best run from MLflow, register model, and deploy using a Transform or endpoint deployment step.
- C
Set up an EventBridge rule to trigger a Lambda that deploys the model whenever a new MLflow run is logged
Why wrong: This is event-driven but does not handle selecting the best run; it would deploy every run.
- D
Manually export the model artifact from MLflow and upload to S3, then create a SageMaker model and endpoint
Why wrong: Manual steps are not efficient and prone to error.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 team uses MLflow on SageMaker for experiment tracking. They want to automatically deploy the best-performing model from an MLflow run to a SageMaker endpoint for real-time inference. What is the MOST efficient way to achieve this?
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
Use SageMaker Pipelines with the MLflow integration to register the model and deploy via a Transform step
The MLflow Model Registry can be integrated with SageMaker via the MLflow plugin for SageMaker, which allows direct deployment from the registry to an endpoint. Alternatively, using SageMaker Pipelines with the MLflow integration is more automated and production-grade.
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.
- ✗
Use AWS Step Functions to trigger an MLflow run and then call SageMaker CreateEndpoint
Why it's wrong here
This is possible but less integrated than Pipelines; Step Functions would require custom glue code.
- ✓
Use SageMaker Pipelines with the MLflow integration to register the model and deploy via a Transform step
Why this is correct
SageMaker Pipelines can automate the workflow: get best run from MLflow, register model, and deploy using a Transform or endpoint deployment step.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set up an EventBridge rule to trigger a Lambda that deploys the model whenever a new MLflow run is logged
Why it's wrong here
This is event-driven but does not handle selecting the best run; it would deploy every run.
- ✗
Manually export the model artifact from MLflow and upload to S3, then create a SageMaker model and endpoint
Why it's wrong here
Manual steps are not efficient and prone to error.
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
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Deployment and Orchestration of ML Workflows — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker Pipelines with the MLflow integration to register the model and deploy via a Transform step — The MLflow Model Registry can be integrated with SageMaker via the MLflow plugin for SageMaker, which allows direct deployment from the registry to an endpoint. Alternatively, using SageMaker Pipelines with the MLflow integration is more automated and production-grade.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 →
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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