easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company trained a model using SageMaker and…
A company trained a model using SageMaker and wants to deploy it with low latency for real-time inference. Which SageMaker feature is MOST suitable?
⚠ Common exam trap
It's easy for candidates to confuse 'Auto Scaling' (a scaling mechanism) with a separate deployment option, or they assume 'Serverless' always provides low latency, ignoring the cold start penalty that makes it unsuitable for real-time inference.
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
✓
SageMaker Real-Time Endpoint
SageMaker Real-Time Endpoint is the most suitable feature for low-latency real-time inference because it provisions dedicated, persistent instances that respond to requests synchronously with predictable latency. This option directly meets the requirement for serving individual predictions with minimal delay, unlike batch or serverless alternatives that introduce higher latency or are designed for asynchronous processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Endpoint with Auto Scaling
Why it's wrong here
Auto scaling is a configuration on a real-time endpoint, not a separate feature.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless inference can have cold start latency and is not ideal for consistent low-latency requirements.
- ✓
SageMaker Real-Time Endpoint
Why this is correct
Real-time endpoints provide low-latency inference suitable for online predictions.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for batch predictions, not real-time low-latency 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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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