- A
Batch transform
Why wrong: Batch transform processes a batch of data files, not individual real-time requests; it's not suitable for on-demand inference.
- B
Real-time endpoint
Why wrong: Real-time endpoints are always running, incurring costs even when idle; not pay-per-inference.
- C
Serverless inference
Why wrong: Serverless inference has a 60-second timeout and 6 MB payload limit, insufficient for 10 MB and 10-minute inference.
- D
Asynchronous inference endpoint
Asynchronous inference can handle large payloads (up to 1 GB) and long timeouts (up to 1 hour), and scales to zero, charging per request.
Question 561 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 data scientist needs to deploy an anomaly detection model that processes large payloads (up to 10 MB per request) and expects inference times of up to 10 minutes. The team wants to minimize cost and only pay per inference. Which TWO SageMaker inference options meet these requirements? (Choose TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Asynchronous inference endpoint
Option D is correct because asynchronous inference endpoints are designed for large payloads (up to 1 GB) and long processing times (up to 1 hour), making them ideal for this 10 MB, 10-minute inference workload. They also follow a pay-per-inference model, charging only for the duration of each inference request, which minimizes cost.
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.
- ✗
Batch transform
Why it's wrong here
Batch transform processes a batch of data files, not individual real-time requests; it's not suitable for on-demand inference.
- ✗
Real-time endpoint
Why it's wrong here
Real-time endpoints are always running, incurring costs even when idle; not pay-per-inference.
- ✗
Serverless inference
Why it's wrong here
Serverless inference has a 60-second timeout and 6 MB payload limit, insufficient for 10 MB and 10-minute inference.
- ✓
Asynchronous inference endpoint
Why this is correct
Asynchronous inference can handle large payloads (up to 1 GB) and long timeouts (up to 1 hour), and scales to zero, charging per request.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
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 often confuse serverless inference with asynchronous inference, but serverless has a 6 MB payload limit and 15-minute timeout, while asynchronous supports up to 1 GB and 1 hour, making it the correct choice for large, long-running payloads.
Detailed technical explanation
How to think about this question
Asynchronous inference endpoints use an internal Amazon SQS queue to decouple the client from the model, allowing the endpoint to scale to zero when idle and only incur costs during active inference. The endpoint returns a 202 status with an output location in S3, which the client polls, and the maximum payload size is 1 GB with a timeout of up to 1 hour, making it suitable for large, long-running workloads like video processing or complex anomaly detection.
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.
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 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: Asynchronous inference endpoint — Option D is correct because asynchronous inference endpoints are designed for large payloads (up to 1 GB) and long processing times (up to 1 hour), making them ideal for this 10 MB, 10-minute inference workload. They also follow a pay-per-inference model, charging only for the duration of each inference request, which minimizes cost.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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