MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist wants to deploy a PyTorch model for real-time inference. Which SageMaker deployment option provides the lowest latency for single-digit millisecond responses?
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
✓
SageMaker Real-Time Inference endpoint
SageMaker Real-Time Inference endpoints (Option A) are optimized for low-latency, real-time predictions, often achieving single-digit millisecond response times because they maintain a persistent endpoint with pre-warmed instances. Option B (SageMaker Asynchronous Inference) is designed for non-real-time workloads with higher latency due to queuing. Option C (SageMaker Serverless Inference) can introduce cold starts and higher latency, especially for sporadic traffic. Option D (SageMaker Batch Transform) is for offline batch processing and not suitable for real-time inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker Real-Time Inference endpoint
Why this is correct
Real-Time endpoints provide the lowest latency for online inference.
- ✗
SageMaker Asynchronous Inference
Why it's wrong here
Asynchronous Inference is for requests with large payloads and higher latency tolerance.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless can have cold start latency, not ideal for single-digit millisecond requirements.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for offline batch predictions, not real-time.
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
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to deploy a PyTorch model for real-time inference with latency under 100 ms. Which AWS service is most suitable?
easy- ✓ A.Amazon SageMaker real-time endpoint
- B.Amazon SageMaker Processing
- C.AWS Lambda with container image
- D.Amazon SageMaker Batch Transform
Why A: Amazon SageMaker real-time endpoints are designed for low-latency inference, typically under 100 ms, by hosting a model behind an HTTPS endpoint that auto-scales based on traffic. They support PyTorch natively via pre-built containers or custom containers, making them the most suitable choice for this requirement.
Variation 2. A data science team is deploying a machine learning model to production using Amazon SageMaker. The model requires real-time inference with low latency. Which SageMaker feature should they use to deploy the model?
easy- A.SageMaker Notebook Instance
- B.SageMaker Batch Transform
- C.SageMaker Autopilot
- ✓ D.SageMaker Realtime Endpoint
Why D: SageMaker Realtime Endpoints are designed for low-latency, synchronous inference, making them the correct choice for serving predictions in real time. They keep the model loaded and ready to respond to individual requests, which is essential for applications requiring immediate responses.
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.