MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A startup wants to deploy a model that has variable traffic patterns, with some periods of no traffic and occasional spikes. They want to pay only for what they use and do not want to manage instances. Which SageMaker inference option should they choose?
⚠ Common exam trap
Many candidates confuse auto-scaling with the ability to scale to zero, but real-time endpoints with auto-scaling still maintain a minimum number of instances, incurring costs during idle periods, whereas serverless inference truly scales 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
Serverless inference is the correct choice because it automatically scales to zero during periods of no traffic and scales up to handle spikes, charging only for the compute time used. This eliminates the need to manage underlying instances, making it ideal for variable and intermittent traffic patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch transform
Why it's wrong here
Batch transform is unsuitable as it performs offline, asynchronous inference on entire datasets, not real-time predictions for individual requests. It cannot serve immediate traffic spikes or handle periods of no traffic for live applications. This option is tempting because it processes data without continuous endpoint management, making it ideal for scenarios like daily fraud detection or pre-calculating recommendations, where a full dataset is processed periodically and immediate latency is not a requirement.
- ✗
Real-time endpoint with auto-scaling
Why it's wrong here
Real-time endpoints have a minimum instance count and incur costs even when idle.
- ✓
Serverless inference
Why this is correct
Serverless inference scales to zero and charges per request, perfect for variable traffic.
- ✗
Multi-model endpoint
Why it's wrong here
Multi-model endpoints still have running instances with associated costs.
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.