Quick Answer
The answer is SageMaker hosting with TensorFlow Serving container, as this is the only SageMaker capability purpose-built to deploy a TensorFlow SavedModel for real-time inference. TensorFlow Serving is an optimized, production-ready serving system that loads the SavedModel format and exposes a gRPC or REST API, and SageMaker provides a pre-built, fully managed Docker image with TensorFlow Serving already configured, eliminating the need to write custom inference code. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s deployment options versus its other services—a common trap is confusing hosting with Ground Truth (data labeling), Model Monitor (drift detection), or Pipelines (workflow orchestration). Remember the key distinction: if you need to serve a TensorFlow model for live predictions, think “TF Serving container.” A useful memory tip is “SavedModel needs Serving”—the format and the container are a matched pair.
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 to SageMaker for real-time inference. The model is a TensorFlow SavedModel. Which SageMaker capability should be used to create an endpoint?
SageMaker hosting with TensorFlow Serving container
SageMaker supports TensorFlow Serving for model deployment.
SageMaker Pipelines
Why wrong: Pipelines is for CI/CD.
SageMaker Model Monitor
Why wrong: Model Monitor detects drift.
SageMaker Ground Truth
Why wrong: Ground Truth is for data labeling.
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 hosting with TensorFlow Serving container
Option A is correct because SageMaker provides managed TensorFlow serving containers. Option B is wrong because SageMaker Ground Truth is for labeling data. Option C is wrong because SageMaker Model Monitor is for monitoring. Option D is wrong because SageMaker Pipelines is for ML workflows.
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 hosting with TensorFlow Serving container
Why this is correct
SageMaker supports TensorFlow Serving for model deployment.
Related concept
Read the scenario before looking for a memorised answer.
SageMaker Pipelines
Why it's wrong here
Pipelines is for CI/CD.
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift.
SageMaker Ground Truth
Why it's wrong here
Ground Truth is for data labeling.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: SageMaker hosting with TensorFlow Serving container — Option A is correct because SageMaker provides managed TensorFlow serving containers. Option B is wrong because SageMaker Ground Truth is for labeling data. Option C is wrong because SageMaker Model Monitor is for monitoring. Option D is wrong because SageMaker Pipelines is for ML workflows.
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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