Quick Answer
The answer is Amazon SageMaker real-time endpoint. This is the correct choice because SageMaker real-time endpoints are purpose-built to deliver low-latency, highly available fraud detection by automatically scaling inference instances across multiple Availability Zones, ensuring sub-second response times for transaction scoring. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of deployment trade-offs: SageMaker manages the infrastructure for you, unlike Amazon ECS which requires manual cluster management, and unlike AWS Lambda which has a 15-minute timeout unsuitable for heavy model loads. A common trap is confusing batch transform with real-time inference—remember that batch transform is for offline predictions on large datasets, not for live requests. Memory tip: think "real-time = endpoint, batch = transform."
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 machine learning engineer needs to deploy a model that performs real-time fraud detection. The model must be highly available and scalable. Which AWS service should be used to host the model?
AWS Lambda
Why wrong: Lambda has time and memory limits.
Amazon ECS with a custom container
Why wrong: Possible but requires manual scaling and monitoring.
Amazon SageMaker batch transform
Why wrong: Not real-time.
Amazon SageMaker real-time endpoint
Purpose-built for real-time inference with auto-scaling.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker real-time endpoint
Option D is correct because Amazon SageMaker real-time endpoints are designed for low-latency, scalable, and highly available model hosting. Option A is wrong because AWS Lambda has limited execution time and is not suitable for heavy inference. Option B is wrong because Amazon ECS can host containers but requires more management; SageMaker is purpose-built. Option C is wrong because SageMaker batch transform is for offline predictions.
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.
AWS Lambda
Why it's wrong here
Lambda has time and memory limits.
Amazon ECS with a custom container
Why it's wrong here
Possible but requires manual scaling and monitoring.
Amazon SageMaker batch transform
Why it's wrong here
Not real-time.
Amazon SageMaker real-time endpoint
Why this is correct
Purpose-built for real-time inference with auto-scaling.
Related concept
Read the scenario before looking for a memorised answer.
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
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker real-time endpoint — Option D is correct because Amazon SageMaker real-time endpoints are designed for low-latency, scalable, and highly available model hosting. Option A is wrong because AWS Lambda has limited execution time and is not suitable for heavy inference. Option B is wrong because Amazon ECS can host containers but requires more management; SageMaker is purpose-built. Option C is wrong because SageMaker batch transform is for offline predictions.
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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