MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?
⚠ Common exam trap
Watch out — candidates often confuse 'auto-scaling' with 'scaling to zero' and incorrectly choose the real-time endpoint with auto-scaling, not realizing that auto-scaling maintains a minimum instance count and cannot reduce to zero.
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
SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Serverless inference
Why this is correct
Serverless inference scales to zero and has configurable max concurrency and memory.
- ✗
Real-time endpoint with auto-scaling
Why it's wrong here
Real-time endpoints scale down to minimum instance count, not zero.
- ✗
Batch transform
Why it's wrong here
Batch transform is for offline processing, not real-time serving.
- ✗
Asynchronous inference
Why it's wrong here
Asynchronous endpoints have a backing instance pool that doesn't scale to zero.
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.