MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is deploying a model to a SageMaker endpoint and needs to optimize for cost while maintaining low latency. Which TWO actions should the data scientist take?
⚠ Common exam trap
Many exam-takers assume 'larger instances' or 'single instance' are cost-saving measures, but the exam tests understanding that cost optimization for variable traffic requires dynamic scaling (Auto Scaling) or fully serverless compute, not static instance choices.
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 SageMaker Serverless Inference
SageMaker Serverless Inference (Option D) automatically scales compute resources based on request volume, charging only for the compute time used during inference. This eliminates the cost of idle provisioned instances, making it ideal for optimizing cost while maintaining low latency for variable 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.
- ✗
Use a larger instance type
Why it's wrong here
Increases cost, may over-provision.
- ✗
Deploy to a single instance
Why it's wrong here
May cause latency spikes and is not cost-optimized.
- ✗
Switch to batch transform
Why it's wrong here
Not suitable for real-time inference.
- ✓
Use SageMaker Serverless Inference
Why this is correct
Pay per inference, scales automatically, cost-effective.
- ✓
Enable Auto Scaling on the endpoint
Why this is correct
Scales based on demand, reduces cost during off-peak.
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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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.