easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company has a model that receives low traffic…
A company has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?
⚠ Common exam trap
AWS often tests the misconception that auto-scaling (Option B) is the most cost-effective for spikes, but the trap is that auto-scaling still requires a baseline of provisioned instances that incur cost even when idle, whereas serverless inference scales to zero and charges only for active compute time.
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
✓
SageMaker Serverless Inference
SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or 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.
- ✓
SageMaker Serverless Inference
Why this is correct
Serverless scales to zero during idle periods and handles spikes, minimizing cost.
- ✗
SageMaker Real-Time Endpoint with Auto Scaling
Why it's wrong here
Auto scaling still requires at least one instance running, incurring baseline cost.
- ✗
SageMaker Multi-Model Endpoint
Why it's wrong here
Multi-model endpoints still run on persistent instances; not serverless.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for batch processing, not real-time inference.
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.