MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is deploying a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (<100 ms). The model is a PyTorch model with custom pre- and post-processing logic. The team uses a SageMaker Model with a custom inference container. After deployment, they observe that the endpoint takes over 500 ms for the first request, but subsequent requests are fast (~50 ms). What is the MOST likely cause?
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
✓
The container has a cold start delay because the model needs to be loaded into memory from Amazon S3 on the first request.
The first request triggers a cold start where the custom inference container initializes and loads the model from Amazon S3 into memory, causing high latency. Subsequent requests are fast because the model remains cached. Option A is wrong because the instance type primarily affects throughput and steady-state latency, not transient cold starts. Option B is wrong because the issue is not about memory exhaustion—the endpoint handles subsequent requests well. Option D is wrong because auto-scaling adds instances but does not eliminate the cold start for the initial request on a new instance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The instance type is too small to handle the model size.
Why it's wrong here
If instance were too small, latency would be consistently high.
- ✗
The model is too large and exceeds the instance memory.
Why it's wrong here
If memory were exceeded, requests would fail, not just be slow initially.
- ✓
The container has a cold start delay because the model needs to be loaded into memory from Amazon S3 on the first request.
Why this is correct
Cold start occurs when no idle instances are available; model loading from S3 adds latency.
- ✗
The endpoint is not configured with auto-scaling.
Why it's wrong here
Auto-scaling affects number of instances, not cold start latency of a single instance.
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
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