MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is deploying a model using Amazon SageMaker. The model endpoint needs to handle real-time inference requests with low latency. The model is a large ensemble of 10 deep learning models, each approximately 500 MB. What is the most cost-effective deployment strategy that meets the low-latency requirement?
⚠ Common exam trap
Candidates often confuse multi-model endpoints with multi-container endpoints or assume that a single endpoint cannot host multiple models, leading them to choose the expensive separate-endpoint approach (Option A) or the memory-inefficient single-endpoint approach (Option B).
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
✓
Use a SageMaker multi-model endpoint to host all models on one or more instances.
A SageMaker multi-model endpoint (MME) allows hosting multiple models on a single or few instances, dynamically loading them from Amazon S3 into memory as needed. This is the most cost-effective option for a large ensemble of 500 MB models because it avoids the expense of separate endpoints or multiple instances per model, while still supporting low-latency real-time inference by keeping frequently used models cached.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy each model to a separate endpoint and use a load balancer.
Why it's wrong here
Deploying 10 endpoints multiplies cost and complexity.
- ✗
Use a single endpoint with multiple instances behind it.
Why it's wrong here
Multiple instances serve the same model, not multiple models, unless using multi-model endpoints.
- ✗
Use a SageMaker batch transform job to process inference requests in batches.
Why it's wrong here
Batch transform is for offline predictions, not real-time.
- ✓
Use a SageMaker multi-model endpoint to host all models on one or more instances.
Why this is correct
Multi-model endpoints efficiently host multiple models on shared instances, reducing cost.
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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