- A
Train the model using a SageMaker training job
Why wrong: The model is already trained.
- B
Create a SageMaker batch transform job
Why wrong: Batch transform is for offline, not real-time.
- C
Create a SageMaker model object that points to the S3 bucket containing the model artifacts and the inference container image
A model object is required to deploy an endpoint.
- D
Create an endpoint configuration specifying the instance type (e.g., ml.p3.2xlarge) and initial instance count
Endpoint configuration defines the infrastructure for the endpoint.
- E
Create a SageMaker endpoint using the endpoint configuration
The endpoint is created from the configuration.
Question 637 of 1,755
MLS-C01 Modeling Practice Question
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 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?
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
Option C is correct because 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.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create a SageMaker endpoint using the endpoint configuration
Why this is correct
The endpoint is created from the configuration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker model object (CreateModel API) registers the S3 path to the model.tar.gz and the registry path of the inference container (e.g., 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:1.13.1-gpu-py39). The endpoint configuration (CreateEndpointConfig) then maps this model to a specific instance type like ml.p3.2xlarge and sets the initial instance count, which directly controls the number of GPU accelerators available for low-latency inference. In real-world scenarios, you might also configure auto-scaling policies and VPC settings to meet latency SLAs.
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?
Modeling — This question tests Modeling — 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 that points to the S3 bucket containing the model artifacts and the inference container image — Option C is correct because 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.
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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