MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer has trained a model using SageMaker and wants to deploy it to a real-time endpoint. The engineer needs to specify the model artifacts, the inference code, and the environment. Which SageMaker resource should the engineer create first?
⚠ Common exam trap
The trap here is thinking that an endpoint configuration or endpoint can be created without a model, but they both depend on the model resource.
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
✓
A SageMaker model
The deployment sequence in SageMaker begins with creating a model, which specifies the model artifacts and the inference container. This model is then referenced in an endpoint configuration, which defines the deployment settings. Finally, the endpoint is created from that configuration. Therefore, the model is the first resource to create.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An endpoint configuration
Why it's wrong here
An endpoint configuration defines the instance type and count for the endpoint, but it requires a SageMaker model to be specified. You cannot create an endpoint configuration without first creating a model. Therefore, it is not the first resource to create.
- ✗
A SageMaker pipeline
Why it's wrong here
A SageMaker pipeline is used to orchestrate ML workflows, such as training and deployment. While it can include steps to create a model, it is not the resource that directly represents the model. The engineer needs to create a model resource first, which could be done within a pipeline or manually.
- ✓
A SageMaker model
Why this is correct
A SageMaker model is the resource that encapsulates the model artifacts, inference code (as a Docker image), and environment variables. It is a prerequisite for creating an endpoint configuration and then an endpoint. Creating the model first is the correct initial step in the deployment process.
- ✗
A SageMaker endpoint
Why it's wrong here
An endpoint is the actual deployed resource that hosts the model for inference. It is created using an endpoint configuration, which in turn requires a model. Thus, the endpoint is the last resource to create, not the first.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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