Question 864 of 835
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MLA-C01 Practice Question: An ML team is deploying a model using SageMaker
An ML team is deploying a model using SageMaker. The model requires GPU inference and must be available in multiple AWS regions for low latency. The team has created a multi-model endpoint with GPU instances. After deployment, they notice high latency spikes when a new model is loaded. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse multi-model endpoint cold-start latency with general endpoint misconfiguration (like instance type or variant count), but the key clue is the timing of the spikes—only when a new model is loaded—which directly points to the on-demand loading behavior of MMEs.
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 team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.
A multi-model endpoint (MME) loads models on demand from Amazon S3 into the instance's memory. When a new model is requested and not already cached, SageMaker must download the model artifacts and load them into GPU memory, which is a time-consuming operation that causes a latency spike for the first inference request. This cold-start behavior is inherent to MMEs and explains the observed spikes.
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 team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.
Why this is correct
Multi-model endpoints load and unload models from memory, causing latency spikes when a new model is accessed.
- ✗
The endpoint is configured with a single production variant, causing all traffic to overload one instance.
Why it's wrong here
A single variant would cause consistent high latency, not spikes only when a new model is loaded.
- ✗
The endpoint is using the wrong instance type that lacks sufficient GPU memory.
Why it's wrong here
If the instance type were insufficient, latency would be consistently high, not just during model loading.
- ✗
The model is too large for the specified container memory, causing swap to disk.
Why it's wrong here
Swap would cause sustained high latency, not just spikes during loading.
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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Last reviewed: Jun 24, 2026
This MLA-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 MLA-C01 exam.
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