hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A financial services company deploys multiple…
A financial services company deploys multiple models on a single Amazon SageMaker endpoint using a multi-model endpoint (MME). The models are stored in Amazon S3. Each model is approximately 500 MB and is loaded on demand. Users report high latency for cold-start scenarios. What should the company do to reduce cold-start latency?
⚠ Common exam trap
Test-takers frequently confuse scaling the number of instances (Option B) with improving per-request latency, but horizontal scaling does not reduce the time to load a model from S3 into memory on a given instance.
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
✓
Configure the endpoint to use a larger 'ModelCacheSize' parameter.
Increasing the 'ModelCacheSize' parameter allows the SageMaker multi-model endpoint to keep more models loaded in memory, reducing the frequency of cold starts where a model must be downloaded from S3 and loaded into memory. This directly addresses the latency issue by caching models that are frequently accessed, avoiding repeated loading overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the instance size to increase the number of instances per unit cost.
Why it's wrong here
Smaller instances may have less memory, increasing disk swapping and latency.
- ✗
Increase the number of instances in the endpoint's auto-scaling group.
Why it's wrong here
More instances spread the load but each still may have cold starts.
- ✗
Deploy each model on a separate endpoint to avoid concurrent loading.
Why it's wrong here
This increases management overhead and cost, and doesn't directly address cold start.
- ✓
Configure the endpoint to use a larger 'ModelCacheSize' parameter.
Why this is correct
Increasing the model cache size allows more models to be cached in memory, reducing load time.
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
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.