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 provisions compute on demand and scales to zero during idle periods, eliminating charges when unused. Its configurable maximum concurrency caps simultaneous invocations, matching the stem's scaling and concurrency constraints. Provisioned endpoints cannot scale to zero, and asynchronous inference targets queued payloads rather than interactive low-latency requests.
- ✗
Real-time endpoint with auto-scaling
Why it's wrong here
Real-time endpoints keep at least one instance provisioned and scale via auto-scaling policies, so they never scale to zero and lack the serverless maximum-concurrency setting. It is tempting for low-latency synchronous inference, but serverless inference is the option that idles at zero cost and enforces concurrency limits.
- ✗
Batch transform
Why it's wrong here
Batch transform runs a one-off job over an entire dataset and then terminates, offering no endpoint, no concurrency setting and no request-driven scaling. It is tempting for offline scoring of large datasets, but the scenario needs an always-available serverless endpoint that scales to zero between requests.
- ✗
Asynchronous inference
Why it's wrong here
Asynchronous inference queues requests and returns results via Amazon S3, and its endpoints do not scale to zero; it suits large payloads with long processing times. It is tempting because it decouples request and response, but serverless inference is the option providing scale-to-zero and configurable maximum concurrency.
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.