MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data science team has trained a PyTorch model for real-time inference and needs to deploy it on AWS with GPU acceleration while minimizing cold-start latency. Which SageMaker inference option should they choose?
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
✓
Real-time endpoint with ml.g4dn instance
Real-time endpoints with GPU instances (e.g., ml.g4dn) provide low latency and support GPU acceleration, suitable for interactive inference. Serverless inference does not support GPU instances, asynchronous inference is for non-real-time, and batch transform is for offline predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Serverless inference
Why it's wrong here
Serverless inference abstracts infrastructure and cannot attach GPU instances, so the PyTorch model would run on CPU only. It suits intermittent, spiky workloads where cold starts are tolerable. The requirement for GPU acceleration rules it out; a real-time endpoint with GPU instances and provisioned concurrency is needed.
- ✗
Batch transform
Why it's wrong here
Batch transform processes an entire dataset offline in a single job and cannot serve individual real-time requests, so it provides no persistent endpoint and no cold-start behaviour to optimise. It is the correct choice for scoring large stored datasets where latency is irrelevant. Real-time GPU inference requires a continuously hosted endpoint.
- ✗
Asynchronous inference endpoint
Why it's wrong here
Asynchronous inference queues requests and returns results via Amazon S3, designed for large payloads and long processing times rather than low-latency interactive responses. It cannot minimise cold-start latency for real-time requests. It would be correct for near-real-time workloads with payloads up to 1 GB and extended processing durations.
- ✓
Real-time endpoint with ml.g4dn instance
Why this is correct
A real-time endpoint keeps the model loaded on a persistent ml.g4dn GPU instance, so inference requests avoid the container initialisation delay that serverless inference incurs. This satisfies the GPU acceleration and minimal cold-start latency constraints simultaneously.
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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About these practice questions
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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.