- A
Use MLflow to track experiments and register models in MLflow's native registry, then export to SageMaker
Why wrong: The requirement is to register in SageMaker Model Registry, not MLflow's registry.
- B
Use SageMaker Experiments for tracking, then manually register the model using SageMaker console
Why wrong: This does not leverage MLflow as required.
- C
Use MLflow tracking server on SageMaker, then use the SageMaker MLflow plugin to register the model in SageMaker Model Registry
The integration enables MLflow tracking and direct registration to SageMaker Model Registry.
- D
MLflow cannot be used with SageMaker; use SageMaker Experiments instead
Why wrong: MLflow can be used with SageMaker via the integration.
Question 740 of 1,000
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 wants to use MLflow on SageMaker to track experiments and manage model lifecycle. They need to register models in the SageMaker Model Registry after training. Which approach allows them to use MLflow for experiment tracking and then register the best model to SageMaker Model Registry?
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 MLflow tracking server on SageMaker, then use the SageMaker MLflow plugin to register the model in SageMaker Model Registry
Option C is correct because the SageMaker MLflow plugin (sagemaker-mlflow) allows you to use an MLflow tracking server hosted on SageMaker for experiment tracking, and then directly register the best model into the SageMaker Model Registry via the plugin's integration. This avoids manual export steps and keeps the model lifecycle management within SageMaker's native registry, which is required by the team's goal.
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 MLflow to track experiments and register models in MLflow's native registry, then export to SageMaker
Why it's wrong here
The requirement is to register in SageMaker Model Registry, not MLflow's registry.
- ✗
Use SageMaker Experiments for tracking, then manually register the model using SageMaker console
Why it's wrong here
This does not leverage MLflow as required.
- ✓
Use MLflow tracking server on SageMaker, then use the SageMaker MLflow plugin to register the model in SageMaker Model Registry
Why this is correct
The integration enables MLflow tracking and direct registration to SageMaker Model Registry.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
MLflow cannot be used with SageMaker; use SageMaker Experiments instead
Why it's wrong here
MLflow can be used with SageMaker via the integration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that MLflow and SageMaker are mutually exclusive or require complex workarounds, when in fact SageMaker provides a first-class MLflow integration via the tracking server and plugin.
Detailed technical explanation
How to think about this question
The SageMaker MLflow plugin works by intercepting MLflow's model registration calls and translating them into SageMaker Model Registry API calls (e.g., CreateModelPackage), allowing models logged with MLflow's log_model() to appear directly in the SageMaker registry. Under the hood, the plugin uses the boto3 SageMaker client to create model package groups and versions, ensuring compatibility with SageMaker's deployment pipelines. A real-world scenario is a team training multiple models with hyperparameter tuning in SageMaker, using MLflow to compare runs, and then automatically registering the best run's model to the SageMaker Model Registry for CI/CD deployment.
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.
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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 MLflow tracking server on SageMaker, then use the SageMaker MLflow plugin to register the model in SageMaker Model Registry — Option C is correct because the SageMaker MLflow plugin (sagemaker-mlflow) allows you to use an MLflow tracking server hosted on SageMaker for experiment tracking, and then directly register the best model into the SageMaker Model Registry via the plugin's integration. This avoids manual export steps and keeps the model lifecycle management within SageMaker's native registry, which is required by the team's goal.
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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