MLS-C01 Modeling Practice Question
A company is deploying a machine learning model for real-time fraud detection. The model must respond within 100ms. Which SageMaker endpoint deployment strategy should be used?
⚠ Common exam trap
Watch out — candidates often confuse serverless or Lambda-based solutions as inherently low-latency, overlooking the cold-start penalty and network overhead that make them unsuitable for sub-100ms real-time inference in SageMaker.
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
✓
Deploy the model to a SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration.
A SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration provides low-latency (sub-100ms) responses by keeping models loaded in memory and routing requests efficiently. This architecture is ideal for real-time fraud detection where multiple models may be needed, and it meets the strict latency requirement without the cold-start overhead of serverless options.
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 the model to a SageMaker Serverless Inference endpoint.
Why it's wrong here
Serverless Inference may have cold start latency exceeding 100ms.
- ✓
Deploy the model to a SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration.
Why this is correct
Multi-Model Endpoints provide low latency and cost efficiency for real-time serving.
- ✗
Deploy the model as an AWS Lambda function with an API Gateway trigger.
Why it's wrong here
Lambda is not a SageMaker endpoint and may have scaling limits for high throughput.
- ✗
Use SageMaker Batch Transform to process requests in batches.
Why it's wrong here
Batch Transform is not designed for real-time inference.
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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