- A
Asynchronous inference endpoint
Why wrong: Asynchronous inference is designed for large payloads and higher latency, not for sub-100 ms predictions.
- B
Real-time endpoint on ml.g4dn.xlarge
Why wrong: Real-time endpoints are always running and incur costs even when idle, not the most cost-effective for low traffic.
- C
Batch transform job
Why wrong: Batch transform is for offline, bulk predictions, not for real-time requests requiring low latency.
- D
Serverless inference with max concurrency set to 10
Serverless inference scales to zero when idle and charges only for the compute time used, making it cost-effective for low and variable traffic.
Question 458 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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 requires less than 100 ms inference latency for real-time predictions. The model is a small PyTorch model that fits in a single GPU. Which SageMaker inference option is MOST cost-effective for this scenario?
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
✓
Serverless inference with max concurrency set to 10
For low latency and occasional traffic, serverless inference is cost-effective because it scales to zero when not in use and charges per inference. Real-time endpoints incur cost even when idle, batch transform is for offline processing, and asynchronous inference has higher latency.
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.
- ✗
Asynchronous inference endpoint
Why it's wrong here
Asynchronous inference is designed for large payloads and higher latency, not for sub-100 ms predictions.
- ✗
Real-time endpoint on ml.g4dn.xlarge
Why it's wrong here
Real-time endpoints are always running and incur costs even when idle, not the most cost-effective for low traffic.
- ✗
Batch transform job
Why it's wrong here
Batch transform is for offline, bulk predictions, not for real-time requests requiring low latency.
- ✓
Serverless inference with max concurrency set to 10
Why this is correct
Serverless inference scales to zero when idle and charges only for the compute time used, making it cost-effective for low and variable traffic.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 MLA-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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Deployment and Orchestration of ML Workflows — study guide chapter
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Deployment and Orchestration of ML Workflows practice questions
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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: Serverless inference with max concurrency set to 10 — For low latency and occasional traffic, serverless inference is cost-effective because it scales to zero when not in use and charges per inference. Real-time endpoints incur cost even when idle, batch transform is for offline processing, and asynchronous inference has higher latency.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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