AIF-C01 Fundamentals of AI and ML Practice Question
A financial institution is deploying a fraud detection model using Amazon SageMaker. The model must be able to handle sudden spikes in inference requests during promotional events while keeping costs low. The team wants to use a serverless architecture to avoid provisioning idle capacity and to scale automatically from zero. However, the inference latency requirement is under 5 seconds for each request. Which SageMaker inference option should they choose?
⚠ Common exam trap
AWS often tests the misconception that serverless inference cannot meet low-latency requirements, but SageMaker Serverless Inference is specifically designed for sub-second to few-second latency, making it suitable for real-time fraud detection scenarios.
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 correct choice because it automatically scales from zero to handle sudden spikes in inference requests, aligning with the requirement to avoid provisioning idle capacity. It also meets the sub-5-second latency requirement for fraud detection, as it is designed for low-latency, on-demand inference without managing underlying infrastructure.
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 Amazon SageMaker Serverless Inference
Why this is correct
Serverless Inference scales automatically from zero and reduces costs during idle periods.
- ✗
Use Amazon SageMaker Multi-Model Endpoints
Why it's wrong here
Multi-Model Endpoints still require provisioned instances and cannot scale to zero.
- ✗
Use Amazon SageMaker real-time endpoints with auto-scaling
Why it's wrong here
Real-time endpoints require provisioned instances and incur costs even when idle; auto-scaling does not scale to zero.
- ✗
Use Amazon SageMaker Asynchronous Inference
Why it's wrong here
Asynchronous Inference is designed for large payloads and higher latency (up to 30 min), not for sub-5 second latency.
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
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