Quick Answer
The answer is to deploy the model on an Amazon SageMaker real-time endpoint. This option is correct because SageMaker real-time endpoints are purpose-built for low-latency inference, consistently delivering sub-100ms response times required for streaming data from Amazon Kinesis Data Streams. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match deployment options to latency requirements, with the common trap being to confuse asynchronous inference or Lambda for real-time workloads. Remember that SageMaker batch transform is for offline predictions, and asynchronous inference introduces higher latency due to queuing, while Lambda cold starts can break sub-100ms SLAs. A simple memory tip: for real-time inference on streaming data, think “endpoint now, not later”—SageMaker real-time endpoints are the only choice that guarantees immediate, low-latency responses.
This MLS-C01 practice question tests your understanding of modeling. 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 machine learning team is deploying a model that performs real-time inference on streaming data from Amazon Kinesis Data Streams. The model requires sub-100ms latency. Which deployment option should the team choose?
Use Amazon SageMaker batch transform
Why wrong: Batch transform is for offline predictions.
Use Amazon SageMaker asynchronous inference
Why wrong: Asynchronous inference has higher latency.
Deploy the model on an Amazon SageMaker real-time endpoint
Real-time endpoints provide low-latency inference.
Deploy a custom inference container on AWS Lambda
Why wrong: Lambda may have cold start issues affecting latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Deploy the model on an Amazon SageMaker real-time endpoint
Amazon SageMaker real-time endpoints provide low-latency inference suitable for sub-100ms requirements. SageMaker batch transform is for offline predictions. SageMaker asynchronous inference is for near-real-time with longer latencies. AWS Lambda alone may not handle model serving efficiently for low latency. Option A: SageMaker real-time endpoint is correct. Option B: SageMaker batch transform is for batch, not real-time. Option C: SageMaker asynchronous inference has higher latency. Option D: AWS Lambda custom inference is possible but may not meet sub-100ms consistently due to cold starts.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use Amazon SageMaker batch transform
Why it's wrong here
Batch transform is for offline predictions.
Use Amazon SageMaker asynchronous inference
Why it's wrong here
Asynchronous inference has higher latency.
Deploy the model on an Amazon SageMaker real-time endpoint
Why this is correct
Real-time endpoints provide low-latency inference.
Related concept
Read the scenario before looking for a memorised answer.
Deploy a custom inference container on AWS Lambda
Why it's wrong here
Lambda may have cold start issues affecting latency.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Deploy the model on an Amazon SageMaker real-time endpoint — Amazon SageMaker real-time endpoints provide low-latency inference suitable for sub-100ms requirements. SageMaker batch transform is for offline predictions. SageMaker asynchronous inference is for near-real-time with longer latencies. AWS Lambda alone may not handle model serving efficiently for low latency. Option A: SageMaker real-time endpoint is correct. Option B: SageMaker batch transform is for batch, not real-time. Option C: SageMaker asynchronous inference has higher latency. Option D: AWS Lambda custom inference is possible but may not meet sub-100ms consistently due to cold starts.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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