- A
SageMaker batch transform
Why wrong: Batch transform is for offline processing of large datasets, not real-time serving.
- B
SageMaker real-time endpoint
Real-time endpoints are always running and can achieve sub-10 ms latency with appropriately sized instances.
- C
SageMaker asynchronous inference
Why wrong: Asynchronous inference is designed for large payloads and longer processing times, not sub-10 ms latency.
- D
SageMaker serverless inference
Why wrong: Serverless inference has a cold start latency that can be several seconds, which would exceed the 10 ms requirement.
Question 671 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 has a model that needs to serve predictions with very low latency (under 10 ms) for a real-time web application. The model is a small ensemble of three neural networks that fits in memory. Which SageMaker inference option is MOST appropriate?
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
✓
SageMaker real-time endpoint
SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them the best fit for a model that must serve predictions in under 10 ms. Since the ensemble of three neural networks fits in memory, a real-time endpoint can keep the model loaded and respond to each request with minimal overhead, typically using HTTPS and the SageMaker InvokeEndpoint API.
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.
- ✗
SageMaker batch transform
Why it's wrong here
Batch transform is for offline processing of large datasets, not real-time serving.
- ✓
SageMaker real-time endpoint
Why this is correct
Real-time endpoints are always running and can achieve sub-10 ms latency with appropriately sized instances.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker asynchronous inference
Why it's wrong here
Asynchronous inference is designed for large payloads and longer processing times, not sub-10 ms latency.
- ✗
SageMaker serverless inference
Why it's wrong here
Serverless inference has a cold start latency that can be several seconds, which would exceed the 10 ms requirement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'low latency' with 'serverless' or 'asynchronous' options, not realizing that serverless inference has cold starts and asynchronous inference adds queueing delays, both of which break the sub-10 ms requirement.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints keep the model container warm and use a load balancer to distribute requests across multiple instances, ensuring consistent sub-10 ms response times when the model fits in memory. Under the hood, the endpoint uses the SageMaker InvokeEndpoint API over HTTPS, and the model is loaded into GPU or CPU memory once, avoiding per-request loading overhead. A real-world scenario is a fraud detection system where each transaction must be scored in milliseconds to avoid user-perceptible delays.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SageMaker real-time endpoint — SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them the best fit for a model that must serve predictions in under 10 ms. Since the ensemble of three neural networks fits in memory, a real-time endpoint can keep the model loaded and respond to each request with minimal overhead, typically using HTTPS and the SageMaker InvokeEndpoint API.
What should I do if I get this MLA-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 MLA-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 MLA-C01 exam.
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