MLS-C01 Practice Question: Machine Learning Implementation and Operations
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?
⚠ Common exam trap
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.
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.
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.
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 |
Go deeper
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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.