MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company wants to deploy a machine learning model that performs real-time inference with sub-second latency. The model is a deep neural network with 500 MB of weights. The inference endpoint must scale to zero when not in use to minimize cost. Which AWS service should the company use?
⚠ Common exam trap
Many candidates confuse SageMaker Serverless Inference with SageMaker real-time endpoints, assuming automatic scaling can reduce costs to zero, but real-time endpoints always require a minimum instance count, whereas Serverless Inference truly scales to zero.
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 Amazon SageMaker Serverless Inference to host the model.
Amazon SageMaker Serverless Inference is designed for workloads with intermittent traffic patterns, automatically scaling to zero when idle and scaling up for real-time requests. It supports models up to 1 GB in size and provides sub-second latency for inference, making it ideal for this 500 MB deep neural network. This service eliminates the need to manage underlying infrastructure while meeting the latency and cost requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model as an AWS Lambda function with provisioned concurrency.
Why it's wrong here
AWS Lambda has a deployment package limit of 250 MB (including layers), so a 500 MB model cannot be deployed directly. Also, provisioned concurrency does not scale to zero.
- ✓
Use Amazon SageMaker Serverless Inference to host the model.
Why this is correct
SageMaker Serverless Inference automatically scales to zero when idle, reducing costs, and can handle sub-second latency for suitable workloads. It also supports large model sizes.
- ✗
Host the model on Amazon ECS with Fargate and use a target tracking scaling policy.
Why it's wrong here
While ECS with Fargate can scale to zero, it requires more operational overhead (e.g., container management, load balancer) and is not optimized for ML inference latency compared to SageMaker Serverless.
- ✗
Create an Amazon SageMaker real-time endpoint with automatic scaling policies.
Why it's wrong here
Real-time endpoints do not scale to zero; they maintain a minimum number of instances, leading to ongoing costs even when not in use.
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 |
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.