easyMultiple Choice
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 Inference scales to zero when idle and provisions capacity automatically during spikes, so the company pays only for actual usage. This suits low-traffic workloads with intermittent bursts, unlike always-on real-time endpoints that bill continuously.
- ✗
SageMaker Real-Time Endpoint with Auto Scaling
Why it's wrong here
Auto Scaling adds instances only after thresholds are breached, so a sudden spike hits a cold, under-provisioned endpoint before scaling completes. It suits predictable, sustained traffic growth rather than instantaneous bursts, where serverless inference scales immediately per request.
- ✗
SageMaker Multi-Model Endpoint
Why it's wrong here
Multi-Model Endpoints host several models behind one endpoint, sharing instance capacity; they do not scale capacity in response to sudden traffic spikes, so requests queue or fail. They suit many low-traffic models consolidated onto shared hosting, not a single model needing burst capacity.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform processes stored datasets as an offline job and cannot serve interactive requests, so sudden live traffic spikes are unhandled. It is the right choice for scheduled, large-scale inference over data in Amazon S3 where no real-time response is needed.
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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About these practice questions
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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.