Quick Answer
The answer is the SageMaker Model and Endpoint Configuration. This is correct because creating a SageMaker endpoint for real-time inference is a two-step process: you first register the trained model artifact (the .tar.gz file from S3) as a SageMaker Model, which defines the container image and inference code, and then you create an Endpoint Configuration that specifies the instance type and initial instance count for hosting. The endpoint itself is then launched from this configuration, making both components mandatory. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of the deployment workflow, often appearing as a trap where candidates confuse the endpoint with the model alone or mistakenly think a separate service like Lambda or API Gateway is required. A common memory tip is to remember the sequence: Model first, then Endpoint Config, then Endpoint—think of it as the "MCE" chain.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 needs to deploy a trained model to Amazon SageMaker for real-time inference. The model is stored as a .tar.gz file in Amazon S3. Which AWS service is used to create a SageMaker endpoint?
SageMaker Model and Endpoint Configuration
You create a Model, then EndpointConfig, then Endpoint.
AWS Lambda
Why wrong: Lambda is not used to create SageMaker endpoints.
AWS CloudFormation
Why wrong: CloudFormation can deploy SageMaker resources but is not the direct service.
Amazon ECS
Why wrong: ECS is for container orchestration, not SageMaker endpoints.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SageMaker Model and Endpoint Configuration
Option B is correct because a SageMaker endpoint requires a model and an endpoint configuration. Options A, C, D are not required for creating 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.
SageMaker Model and Endpoint Configuration
Why this is correct
You create a Model, then EndpointConfig, then Endpoint.
Related concept
Read the scenario before looking for a memorised answer.
AWS Lambda
Why it's wrong here
Lambda is not used to create SageMaker endpoints.
AWS CloudFormation
Why it's wrong here
CloudFormation can deploy SageMaker resources but is not the direct service.
Amazon ECS
Why it's wrong here
ECS is for container orchestration, not SageMaker endpoints.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: SageMaker Model and Endpoint Configuration — Option B is correct because a SageMaker endpoint requires a model and an endpoint configuration. Options A, C, D are not required for creating the endpoint.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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