MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO factors should be considered when choosing between Amazon SageMaker's real-time endpoints and serverless inference? (Select TWO.)
⚠ Common exam trap
Candidates often mistakenly think serverless inference cannot handle large models or lacks Lambda integration, but the real differentiators are GPU support and traffic pattern suitability.
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
✓
GPU requirement
GPU requirement is a key factor because SageMaker real-time endpoints support GPU-based instances (e.g., ml.p3, ml.g4dn) for low-latency inference on deep learning models, while serverless inference only supports CPU instances. If your model requires GPU acceleration for acceptable latency, you must choose a real-time endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
GPU requirement
Why this is correct
Serverless inference does not support GPU instances.
- ✓
Inference traffic pattern (intermittent vs steady)
Why this is correct
Serverless is cost-effective for intermittent traffic; real-time endpoints are for steady traffic.
- ✗
Integration with AWS Lambda
Why it's wrong here
Both can be invoked via Lambda; not a deciding factor.
- ✗
Availability of built-in algorithms
Why it's wrong here
Both support built-in and custom containers.
- ✗
Model size in GB
Why it's wrong here
Both have size limits; serverless has a 6 GB memory limit, but model size is always a factor.
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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