- A
Register the model in SageMaker Model Registry.
Why wrong: Model Registry is optional for deployment.
- B
Create a SageMaker training job to re-train the model.
Why wrong: A training job is not needed for deployment.
- C
Save the model as a SavedModel format.
Why wrong: The model is already in SavedModel format in S3.
- D
Create a SageMaker Model object using the TensorFlow serving image.
A SageMaker Model object is required to specify the container and artifact location for deployment.
Question 932 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 needs to deploy a TensorFlow model to a SageMaker endpoint. The model expects a specific input format. The engineer has the model artifacts stored in an S3 bucket. Which step is REQUIRED to deploy the model?
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 object using the TensorFlow serving image.
To deploy a TensorFlow model to a SageMaker endpoint, you must create a SageMaker Model object that references the model artifacts in S3 and specifies the appropriate TensorFlow Serving container image. This image handles loading the model and exposing a RESTful or gRPC inference endpoint. Without this step, SageMaker cannot associate the artifacts with a serving container to start the endpoint.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Register the model in SageMaker Model Registry.
Why it's wrong here
Model Registry is optional for deployment.
- ✗
Create a SageMaker training job to re-train the model.
Why it's wrong here
A training job is not needed for deployment.
- ✗
Save the model as a SavedModel format.
Why it's wrong here
The model is already in SavedModel format in S3.
- ✓
Create a SageMaker Model object using the TensorFlow serving image.
Why this is correct
A SageMaker Model object is required to specify the container and artifact location for deployment.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that model artifacts must be in a specific format (like SavedModel) before deployment, but the question explicitly states the artifacts are already stored, so the required step is the SageMaker Model object creation, not the format conversion.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker uses the specified container image (e.g., 763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-inference:2.12-cpu) to launch an inference endpoint that loads the model from the S3 path defined in the Model object. The container expects the model artifacts to be in a specific directory structure (e.g., /opt/ml/model/1/saved_model.pb) and automatically starts TensorFlow Serving. A real-world scenario where this matters is when deploying a model with custom preprocessing logic, requiring a custom inference script or container, but the core step remains creating the Model object.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a SageMaker Model object using the TensorFlow serving image. — To deploy a TensorFlow model to a SageMaker endpoint, you must create a SageMaker Model object that references the model artifacts in S3 and specifies the appropriate TensorFlow Serving container image. This image handles loading the model and exposing a RESTful or gRPC inference endpoint. Without this step, SageMaker cannot associate the artifacts with a serving container to start the endpoint.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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