MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data science team is deploying a PyTorch model for real-time inference with sub-second latency requirements. They need to minimize cost while handling variable traffic. Which TWO approaches should they consider? (Choose TWO.)
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
✓
Compile the model with SageMaker Neo
Serverless inference auto-scales to zero when not in use and charges per request, minimizing cost for variable traffic. SageMaker Neo compiles the model for optimal hardware performance, achieving low latency. Multi-model endpoints (MME) are for hosting multiple models, not single-model optimization. Elastic Inference adds GPU acceleration at lower cost than a full GPU instance, but with Neo compilation the team may not need it. Batch transform is for offline, not real-time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Compile the model with SageMaker Neo
Why this is correct
Neo optimizes the model for the target hardware, reducing inference latency and often allowing a smaller instance type.
- ✗
Attach Amazon Elastic Inference to a real-time endpoint
Why it's wrong here
Elastic Inference adds GPU acceleration but incurs additional cost; with Neo compilation, the team may not need it and the combination is not the most cost-effective.
- ✗
Use a batch transform job to process requests in batches
Why it's wrong here
Batch transform is for offline, asynchronous processing, not real-time inference.
- ✓
Use SageMaker serverless inference with a configured max concurrency
Why this is correct
Serverless inference scales automatically and charges per invocation, ideal for variable traffic. It can achieve sub-second latency for small models.
- ✗
Use a multi-model endpoint (MME) to host the model
Why it's wrong here
MME is designed for hosting many models on one endpoint, not for optimizing a single model's latency or cost.
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
This MLA-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 MLA-C01 exam.