MLS-C01 Modeling Practice Question
A company is deploying a machine learning model on SageMaker for real-time inference. The model requires GPU for low latency. Which THREE steps are necessary to set up the endpoint?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between batch transform and real-time endpoints, and candidates mistakenly think a batch transform job is required for deploying a real-time endpoint, but it is only for offline inference.
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 that points to the S3 bucket containing the model artifacts and the inference container image
To deploy a model for real-time inference on SageMaker, you must first create a SageMaker model object that references the model artifacts stored in S3 and the inference container image (e.g., a GPU-enabled Docker image). This object is the foundational resource that SageMaker uses to launch instances for serving predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the model using a SageMaker training job
Why it's wrong here
The model is already trained.
- ✗
Create a SageMaker batch transform job
Why it's wrong here
Batch transform is for offline, not real-time.
- ✓
Create a SageMaker model object that points to the S3 bucket containing the model artifacts and the inference container image
Why this is correct
A model object is required to deploy an endpoint.
- ✓
Create an endpoint configuration specifying the instance type (e.g., ml.p3.2xlarge) and initial instance count
Why this is correct
Endpoint configuration defines the infrastructure for the endpoint.
- ✓
Create a SageMaker endpoint using the endpoint configuration
Why this is correct
The endpoint is created from the configuration.
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 |
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JA
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.