When to Use SageMaker Serverless Inference for Bursty Small Requests
A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests that are small but arrive in bursts. The data scientist wants to minimize latency and cost. Which deployment option is MOST appropriate?
Quick Answer
The answer is to use Amazon SageMaker Serverless Inference for bursty small requests. This deployment option is correct because it automatically scales from zero to handle traffic spikes, charges only for the compute time consumed per millisecond, and incurs zero idle costs—perfectly matching the pattern of small, intermittent payloads where a provisioned instance would waste money between bursts. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of cost-optimized inference architectures; a common trap is choosing a real-time endpoint with auto-scaling, which still incurs a base cost for the underlying instances even at low traffic. Remember the key trade-off: serverless excels for unpredictable, short-lived workloads, while provisioned endpoints suit steady, high-throughput traffic. For the exam, think “bursty and small? Serverless handles it all.”
⚠ Common exam trap
Many exam-takers confuse 'multi-model endpoints' with 'serverless' and assume auto-scaling eliminates idle costs, but multi-model endpoints still require a minimum number of running instances, incurring continuous charges.
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
✓
Use Amazon SageMaker Serverless Inference
Amazon SageMaker Serverless Inference is the most appropriate option because it automatically scales compute resources based on request volume, charges only for the compute time used during inference (per-millisecond billing), and has no idle costs. This matches the bursty, small-request pattern perfectly, minimizing both latency and cost without requiring manual instance management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a real-time endpoint with a single instance
Why it's wrong here
Single instance may not handle bursts and incurs cost even when idle.
- ✗
Use a multi-model endpoint with auto-scaling
Why it's wrong here
Multi-model endpoints are for hosting multiple models, not specifically for burst handling.
- ✓
Use Amazon SageMaker Serverless Inference
Why this is correct
Serverless scales automatically and you pay only for inference duration.
- ✗
Use a batch transform job triggered by a schedule
Why it's wrong here
Batch transform is for offline predictions, 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 |
Go deeper
Related to this question
About these practice questions
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is deploying a fraud detection model using Amazon SageMaker. The model is a linear learner trained on 100 GB of data. For inference, the model receives individual transactions and must return a prediction within 100 ms. Which endpoint configuration should the team use to meet the latency requirement?
medium- A.Use a multi-model endpoint with CPU instances.
- ✓ B.Deploy a single model endpoint using a GPU instance and enable autoscaling.
- C.Use a batch transform job scheduled every minute.
- D.Deploy using SageMaker Serverless Inference.
Why B: A single-model endpoint on a GPU instance provides the low-latency, high-throughput inference required for real-time fraud detection. GPU instances accelerate linear learner inference by parallelizing matrix operations, enabling sub-100 ms predictions for individual transactions. Autoscaling ensures the endpoint can handle traffic spikes without degrading latency.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.