- A
RegisterModelStep
RegisterModelStep registers a trained model into the Model Registry.
- B
CreateModelStep
Why wrong: CreateModelStep creates a SageMaker Model object but does not register it in the registry.
- C
TrainingStep
Why wrong: TrainingStep only trains; it doesn't register the model.
- D
TransformStep
Why wrong: TransformStep runs batch inference, not model registration.
Question 290 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 data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?
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
✓
RegisterModelStep
The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.
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.
- ✓
RegisterModelStep
Why this is correct
RegisterModelStep registers a trained model into the Model Registry.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
CreateModelStep
Why it's wrong here
CreateModelStep creates a SageMaker Model object but does not register it in the registry.
- ✗
TrainingStep
Why it's wrong here
TrainingStep only trains; it doesn't register the model.
- ✗
TransformStep
Why it's wrong here
TransformStep runs batch inference, not model registration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse `CreateModelStep` (which creates a deployable model resource) with `RegisterModelStep` (which creates a model package version in the registry), assuming both serve the same purpose of model registration.
Detailed technical explanation
How to think about this question
Under the hood, `RegisterModelStep` calls the `CreateModelPackage` API, which creates a model package version in a specified model package group. It automatically infers the inference container image from the training step's framework version (e.g., PyTorch) and can include additional metadata like approval status, description, and evaluation metrics. A real-world scenario is a CI/CD pipeline where the `RegisterModelStep` is followed by a `ConditionStep` to check model quality metrics before auto-approving the model for deployment, enabling MLOps governance.
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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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: RegisterModelStep — The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.
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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