- A
Upload the training data to S3
Why wrong: Uploading training data to S3 is a prerequisite for training, not for deployment after training is complete.
- B
Create a custom Docker image
Why wrong: Creating a custom Docker image is only required when using a custom inference container; built‑in algorithms do not need it.
- C
Retrain the model with more data
Why wrong: Retraining with more data is a separate activity and not a step in the deployment workflow.
- D
Register the model in SageMaker Model Registry
Registering the model in the Model Registry is an optional step for version management; it is not required before deployment. The essential step is to create a SageMaker Model object.
Question 444 of 1,755
The Essential Step Before Deploying a SageMaker Endpoint: Model Registry Registration
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 has trained a model using SageMaker and wants to deploy it to an endpoint. Which step is required before deployment?
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
✓
Register the model in SageMaker Model Registry
Before deploying a trained model to a SageMaker endpoint, you must create a SageMaker Model object. This can be done via the 'Create model' API or by using the Model Registry, but the registry is optional. None of the provided options describe the required step: uploading training data (A) is done before training, creating a custom Docker image (B) is only needed for custom containers, retraining (C) is unrelated to deployment, and registering in the Model Registry (D) is not mandatory because you can create a model directly.
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.
- ✗
Upload the training data to S3
Why it's wrong here
Uploading training data to S3 is a prerequisite for training, not for deployment after training is complete.
- ✗
Create a custom Docker image
Why it's wrong here
Creating a custom Docker image is only required when using a custom inference container; built‑in algorithms do not need it.
- ✗
Retrain the model with more data
Why it's wrong here
Retraining with more data is a separate activity and not a step in the deployment workflow.
- ✓
Register the model in SageMaker Model Registry
Why this is correct
Registering the model in the Model Registry is an optional step for version management; it is not required before deployment. The essential step is to create a SageMaker Model object.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume that Model Registry registration is mandatory for deployment, but SageMaker allows direct creation of a Model object without registry. The key required step is having a Model resource.
Detailed technical explanation
How to think about this question
The SageMaker Model Registry stores model metadata, including the S3 URI of the model artifact, the inference image URI, and optional versioning and approval status. When you call `create_endpoint_config`, SageMaker references the registered model to pull the artifact and container, then provisions EC2 instances behind the endpoint. A real‑world scenario: a team trains multiple versions of a model and uses the registry to track which version is approved for production, ensuring only validated models are deployed.
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 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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Register the model in SageMaker Model Registry — Before deploying a trained model to a SageMaker endpoint, you must create a SageMaker Model object. This can be done via the 'Create model' API or by using the Model Registry, but the registry is optional. None of the provided options describe the required step: uploading training data (A) is done before training, creating a custom Docker image (B) is only needed for custom containers, retraining (C) is unrelated to deployment, and registering in the Model Registry (D) is not mandatory because you can create a model directly.
What should I do if I get this MLS-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 MLS-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 MLS-C01 exam.
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