MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company plans to deploy a large foundation model using SageMaker JumpStart. They are concerned about costs because the model will be used intermittently. Which deployment option is MOST cost-effective for intermittent traffic?
⚠ Common exam trap
Watch out — candidates often confuse 'multi-model endpoints' with 'serverless' and assume they both scale to zero, but multi-model endpoints still run on provisioned instances that incur hourly costs regardless of traffic.
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
✓
Deploy as a serverless endpoint
Serverless endpoints in SageMaker automatically scale to zero when not in use, so you pay only for the compute time consumed during inference requests. This makes them the most cost-effective option for intermittent traffic, as you avoid paying for idle compute capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Purchase SageMaker Savings Plans for the endpoint
Why it's wrong here
Savings Plans provide discounts for consistent usage but require a commitment; they do not reduce cost for idle time.
- ✓
Deploy as a serverless endpoint
Why this is correct
Serverless endpoints scale down to zero during inactivity, reducing costs for intermittent usage.
- ✗
Use a batch transform job for each request
Why it's wrong here
Batch transform is designed for bulk inference on a dataset, not for real-time or intermittent inference requests.
- ✗
Deploy as a real-time endpoint with a multi-model endpoint
Why it's wrong here
Multi-model endpoints do not scale to zero; they incur costs even when idle.
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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JA
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.