MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model on Amazon SageMaker. Which TWO steps are required to create a SageMaker endpoint?
⚠ Common exam trap
Many candidates confuse the training job (Option B) as a prerequisite for deployment, but SageMaker allows deploying a pre-trained model without ever running a training job, so only the model creation and endpoint configuration are mandatory.
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
✓
Create a SageMaker model
A is correct because creating a SageMaker model is the first required step to define the model artifacts, inference code, and container image that will be used for predictions. Without a model object, SageMaker has no executable artifact to deploy behind the endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a SageMaker model
Why this is correct
Model must be registered first.
- ✗
Submit a training job
Why it's wrong here
Training is separate from deployment.
- ✗
Create a SageMaker pipeline
Why it's wrong here
Pipeline is for orchestration, not required.
- ✓
Create an endpoint configuration
Why this is correct
Endpoint configuration specifies instance type and model.
- ✗
Create a SageMaker notebook instance
Why it's wrong here
Notebook instance is for development, not deployment.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.