Quick Answer
The answer is to create an endpoint configuration. This step is required because the endpoint configuration defines the production variant, specifying which SageMaker model to deploy, the instance type, and the initial instance count for the real-time inference endpoint. Without first creating a SageMaker model object—which bundles the model artifacts, inference code, and container image—SageMaker has no executable artifact to route traffic to, making the endpoint configuration the critical orchestration step that ties the model to compute resources. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of the deployment pipeline: many candidates mistakenly think creating the endpoint itself is the first step, but the exam emphasizes that the endpoint configuration must exist before the endpoint can be launched. A common trap is confusing the model creation step with the endpoint configuration step—remember that the model is the artifact, while the configuration is the deployment blueprint. Use the mnemonic "Model first, then Config, then Endpoint" to lock in the sequence.
This MLS-C01 practice question tests your understanding of modeling. 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 machine learning engineer is deploying a model on Amazon SageMaker. Which TWO steps are required to create a SageMaker endpoint?
Create a SageMaker model
Model must be registered first.
Submit a training job
Why wrong: Training is separate from deployment.
Create a SageMaker pipeline
Why wrong: Pipeline is for orchestration, not required.
Create an endpoint configuration
Endpoint configuration specifies instance type and model.
Create a SageMaker notebook instance
Why wrong: Notebook instance is for development, not deployment.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Create a SageMaker notebook instance
Why it's wrong here
Notebook instance is for development, not deployment.
Common exam traps
The trap here is that 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.
Detailed technical explanation
Under the hood, the SageMaker model object stores a reference to the Docker registry path (e.g., 763104351884.dkr.ecr.us-east-1.amazonaws.com) and the S3 path to model.tar.gz. The endpoint configuration then defines instance type (e.g., ml.m5.large), initial instance count, and variant weights. A real-world scenario where this matters is when you need to deploy multiple variants for A/B testing — the endpoint configuration allows traffic splitting between different model versions.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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