MLS-C01 Modeling Practice Question
A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 4 GB of memory and has a latency requirement of 100 ms. Which instance type and deployment configuration should the engineer choose to optimize cost while meeting requirements?
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
✓
ml.m5.large (2 vCPU, 8 GB memory)
ml.m5.large provides 8 GB memory, sufficient for a 4 GB model plus overhead, and is cost-effective for real-time inference with moderate latency requirements. Option B (SageMaker Serverless Inference) is incorrect because cold start latency may exceed the 100 ms requirement. Option C (ml.c5.large) has only 4 GB memory, insufficient for the model. Option D (ml.p3.2xlarge) is GPU-accelerated and expensive, making it overkill for a non-GPU workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
ml.m5.large (2 vCPU, 8 GB memory)
Why this is correct
8 GB memory provides headroom, and cost is moderate.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless may introduce cold start latency exceeding 100 ms.
- ✗
ml.c5.large (2 vCPU, 4 GB memory)
Why it's wrong here
4 GB memory may be insufficient for the model and runtime overhead.
- ✗
ml.p3.2xlarge (8 vCPU, 61 GB memory, 1 GPU)
Why it's wrong here
GPU instances are costly and unnecessary for this model.
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 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.