MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company wants to use SageMaker to host multiple models behind a single endpoint to reduce costs. Which SageMaker feature should they use?
⚠ Common exam trap
Candidates often confuse SageMaker Multi-Model Endpoints with multi-container endpoints, but multi-container endpoints run multiple containers per instance for a single pipeline, not independently serving different models on demand.
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
✓
SageMaker Multi-Model Endpoints
SageMaker Multi-Model Endpoints allow you to deploy multiple models behind a single endpoint, each loaded dynamically from Amazon S3 based on the inference request. This reduces hosting costs by sharing a single instance across many models, as only the models that are actively invoked consume memory. The correct answer is E because this feature is specifically designed for cost-efficient multi-model hosting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Elastic Inference
Why it's wrong here
Elastic Inference accelerates inference but does not host multiple models.
- ✗
SageMaker inference pipeline
Why it's wrong here
Inference pipeline is for preprocessing and prediction in sequence.
- ✗
SageMaker batch transform
Why it's wrong here
Batch transform is for offline processing, not real-time.
- ✗
SageMaker multi-container endpoints
Why it's wrong here
Multi-container is for serving multiple containers per endpoint, not multiple models.
- ✓
SageMaker Multi-Model Endpoints
Why this is correct
Multi-Model Endpoints host multiple models on the same endpoint.
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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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.