MLS-C01 Modeling Practice Question
A company is using SageMaker to deploy a real-time inference endpoint for a natural language processing model. The model receives input text and returns predictions. The data scientist notices that the endpoint latency increases significantly under load. Which design change would MOST effectively reduce latency?
⚠ Common exam trap
It's easy for candidates to assume scaling out (Option C) is the universal fix for latency, but the question specifically targets latency under load caused by preprocessing overhead, not throughput limits.
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 an inference pipeline to combine preprocessing and model inference
An inference pipeline in SageMaker allows you to chain preprocessing logic directly with the model inference within the same endpoint container. This eliminates the need for separate Lambda functions or client-side preprocessing, which reduces network round-trips and serialization overhead, thereby lowering latency under load.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable data capture for monitoring
Why it's wrong here
Data capture adds overhead and latency.
- ✗
Switch to batch transform for real-time predictions
Why it's wrong here
Batch transform is for offline predictions, not real-time.
- ✗
Increase the number of instances behind the endpoint
Why it's wrong here
More instances improve throughput but not per-request latency.
- ✓
Use an inference pipeline to combine preprocessing and model inference
Why this is correct
Inference pipelines reduce network overhead between preprocessing and prediction.
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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