MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using SageMaker to host a model that makes predictions on streaming data from Amazon Kinesis. The model must provide predictions with sub-second latency. Which approach should the company use?
⚠ Common exam trap
It's easy for candidates to confuse asynchronous inference with real-time inference, assuming that any serverless trigger (like Kinesis) automatically provides low latency, but asynchronous inference is designed for batch-like, non-real-time workloads.
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 a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis
A SageMaker real-time endpoint provides sub-second latency for individual predictions, and invoking it from an AWS Lambda function triggered by Kinesis allows each streaming record to be processed synchronously with low overhead. This architecture meets the requirement for low-latency predictions on streaming data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker asynchronous inference with a Kinesis trigger
Why it's wrong here
Async inference has higher latency, not sub-second.
- ✓
Use a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis
Why this is correct
Real-time endpoint plus Lambda provides sub-second latency.
- ✗
Use Amazon Kinesis Data Analytics with a built-in ML model
Why it's wrong here
Does not use SageMaker model.
- ✗
Use SageMaker batch transform to process batches of records from Kinesis
Why it's wrong here
Batch transform is 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 |
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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.