- A
Use SageMaker asynchronous inference with a Kinesis trigger
Why wrong: Async inference has higher latency, not sub-second.
- B
Use a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis
Real-time endpoint plus Lambda provides sub-second latency.
- C
Use Amazon Kinesis Data Analytics with a built-in ML model
Why wrong: Does not use SageMaker model.
- D
Use SageMaker batch transform to process batches of records from Kinesis
Why wrong: Batch transform is not real-time.
Question 1,432 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, a SageMaker real-time endpoint runs a containerized model behind an HTTPS endpoint with auto-scaling, and the Lambda function acts as a synchronous bridge: it receives records from Kinesis via the event source mapping, invokes the endpoint using the boto3 SageMaker Runtime client (invoke_endpoint API), and returns the prediction. The key is that the Lambda function must be configured with a reserved concurrency and a short timeout (e.g., 5 seconds) to avoid throttling and ensure sub-second response times, while the endpoint must have sufficient instance count to handle peak throughput.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis — Option B is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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