MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer needs to deploy a model that requires less than 100 ms inference latency for real-time predictions. The model is a small PyTorch model that fits in a single GPU. Which SageMaker inference option is MOST cost-effective for this scenario?
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
✓
Serverless inference with max concurrency set to 10
For low latency and occasional traffic, serverless inference is cost-effective because it scales to zero when not in use and charges per inference. Real-time endpoints incur cost even when idle, batch transform is for offline processing, and asynchronous inference has higher latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Asynchronous inference endpoint
Why it's wrong here
Asynchronous inference is designed for large payloads and higher latency, not for sub-100 ms predictions.
- ✗
Real-time endpoint on ml.g4dn.xlarge
Why it's wrong here
Real-time endpoints are always running and incur costs even when idle, not the most cost-effective for low traffic.
- ✗
Batch transform job
Why it's wrong here
Batch transform is for offline, bulk predictions, not for real-time requests requiring low latency.
- ✓
Serverless inference with max concurrency set to 10
Why this is correct
Serverless inference scales to zero when idle and charges only for the compute time used, making it cost-effective for low and variable traffic.
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
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.