MLS-C01 Modeling Practice Question
A machine learning team is deploying a real-time inference endpoint for a recommendation model using Amazon SageMaker. The model takes a long time to load (several minutes) due to its size (5 GB). Which deployment strategy minimizes the cold start latency?
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 Multi-Model Endpoints to keep the model loaded between invocations
Multi-Model Endpoints (MME) allow multiple models to be loaded on the same endpoint and keep them cached in memory between invocations, reducing cold start latency for subsequent calls. This is ideal for large models like the 5 GB recommendation model. Option A (single instance with large memory) does not address load time. Option C (Serverless Inference) incurs cold starts on each invocation. Option D (larger instance type with more vCPUs) may speed up loading but does not prevent cold starts after idle periods. Thus, MMEs minimize cold start by maintaining model persistence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a single instance with a large memory size
Why it's wrong here
While larger memory helps, loading time is still an issue.
- ✓
Use Multi-Model Endpoints to keep the model loaded between invocations
Why this is correct
Multi-Model Endpoints allow models to stay loaded in memory, reducing cold start.
- ✗
Use SageMaker Serverless Inference
Why it's wrong here
Serverless Inference has cold start latency that can be high for large models.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
More vCPUs may not significantly reduce model loading time if the bottleneck is disk I/O or memory bandwidth.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.