- A
SageMaker batch transform
Why wrong: Batch transform is a batch, not event-driven, process. It requires scheduling and is not triggered by new S3 objects.
- B
SageMaker asynchronous inference
Asynchronous inference is ideal for near-real-time, event-driven workloads with S3 input/output and scales to zero when idle.
- C
SageMaker real-time endpoint with auto scaling
Why wrong: Real-time endpoints incur cost even when idle; auto scaling can only scale down to one instance, not zero.
- D
SageMaker serverless inference
Why wrong: Serverless inference is best for short, infrequent requests with small payloads. Image processing may exceed the maximum payload size (6 MB) and memory limits.
Question 756 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. 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 company wants to deploy a single model that processes images from a production line. The images are uploaded to an S3 bucket every few minutes, and the inference results must be stored back to S3. The team wants to avoid paying for idle compute and prefers a fully managed, on-demand solution. Which SageMaker inference option should they use?
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 asynchronous inference
Asynchronous inference is designed for this use case: it processes images from S3 input, writes results to S3 output, scales to zero when idle, and is fully managed. Real-time endpoints are always running and incur cost when idle. Batch transform is not event-driven. Serverless inference is event-driven but has a payload limit and cold start that may not be suitable for image payloads.
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 a batch, not event-driven, process. It requires scheduling and is not triggered by new S3 objects.
- ✓
SageMaker asynchronous inference
Why this is correct
Asynchronous inference is ideal for near-real-time, event-driven workloads with S3 input/output and scales to zero when idle.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker real-time endpoint with auto scaling
Why it's wrong here
Real-time endpoints incur cost even when idle; auto scaling can only scale down to one instance, not zero.
- ✗
SageMaker serverless inference
Why it's wrong here
Serverless inference is best for short, infrequent requests with small payloads. Image processing may exceed the maximum payload size (6 MB) and memory limits.
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
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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: SageMaker asynchronous inference — Asynchronous inference is designed for this use case: it processes images from S3 input, writes results to S3 output, scales to zero when idle, and is fully managed. Real-time endpoints are always running and incur cost when idle. Batch transform is not event-driven. Serverless inference is event-driven but has a payload limit and cold start that may not be suitable for image payloads.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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