- A
AWS Lambda
Why wrong: Lambda has time and memory limits.
- B
Amazon ECS with a custom container
Why wrong: Possible but requires manual scaling and monitoring.
- C
Amazon SageMaker batch transform
Why wrong: Not real-time.
- D
Amazon SageMaker real-time endpoint
Purpose-built for real-time inference with auto-scaling.
Question 1,407 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 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?
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
✓
Amazon SageMaker real-time endpoint
Amazon SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them ideal for real-time fraud detection. They automatically scale across multiple instances and Availability Zones, providing high availability and elasticity to handle variable traffic loads without manual intervention.
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.
- ✗
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse batch transform with real-time inference, or assume Lambda can handle persistent, low-latency model serving without considering its timeout and payload size limits.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints use Amazon Elastic Inference or GPU instances to accelerate inference, and they integrate with AWS Auto Scaling to adjust capacity based on CloudWatch metrics like invocation count or latency. Under the hood, each endpoint deploys one or more models behind a load balancer, and you can configure a production variant to route traffic for A/B testing. In a real-world fraud detection pipeline, the endpoint would be called from a web API via the SageMaker Runtime InvokeEndpoint API, with response times typically under 100 ms.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Amazon SageMaker real-time endpoint — Amazon SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them ideal for real-time fraud detection. They automatically scale across multiple instances and Availability Zones, providing high availability and elasticity to handle variable traffic loads without manual intervention.
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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