- A
Serverless inference
Serverless inference scales to zero and has configurable max concurrency and memory.
- B
Real-time endpoint with auto-scaling
Why wrong: Real-time endpoints scale down to minimum instance count, not zero.
- C
Batch transform
Why wrong: Batch transform is for offline processing, not real-time serving.
- D
Asynchronous inference
Why wrong: Asynchronous endpoints have a backing instance pool that doesn't scale to zero.
Question 857 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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?
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
SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.
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.
- ✓
Serverless inference
Why this is correct
Serverless inference scales to zero and has configurable max concurrency and memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Real-time endpoint with auto-scaling
Why it's wrong here
Real-time endpoints scale down to minimum instance count, not zero.
- ✗
Batch transform
Why it's wrong here
Batch transform is for offline processing, not real-time serving.
- ✗
Asynchronous inference
Why it's wrong here
Asynchronous endpoints have a backing instance pool that doesn't scale to zero.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'auto-scaling' with 'scaling to zero' and incorrectly choose the real-time endpoint with auto-scaling, not realizing that auto-scaling maintains a minimum instance count and cannot reduce to zero.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Serverless Inference uses AWS Lambda-like infrastructure to allocate compute resources on-demand per invocation, with memory sizes ranging from 1024 MB to 6144 MB and concurrency limits from 1 to 200 per endpoint. A subtle behavior is that cold starts can occur when scaling from zero, adding latency to the first request after idle periods, which is a key trade-off for cost savings. In real-world scenarios, this option is ideal for chatbots or lightweight prediction APIs that experience long idle intervals, where the pay-per-invoke model drastically reduces costs compared to always-on instances.
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 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.
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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 — SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.
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.
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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