MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer is deploying a model to a SageMaker endpoint that must handle occasional large payloads up to 1 GB. The inference time can take up to 10 minutes. The team wants to minimize cost and avoid idle compute. Which deployment option is most appropriate?
⚠ Common exam trap
The trap here is assuming that real-time endpoints can handle any payload size, but they are limited to 6 MB and are cost-inefficient for long-running jobs.
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 large payloads (up to 1 GB) and long inference times (up to 15 minutes). It queues requests and returns results via S3, and it can scale to zero when idle, reducing cost. Real-time and serverless inference have payload and timeout limits, and batch transform is for offline batch processing, not on-demand requests.
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 real-time endpoint with automatic scaling
Why it's wrong here
Real-time endpoints are designed for low-latency, synchronous inference and have a maximum payload size of 6 MB for InvokeEndpoint. They are not suitable for 1 GB payloads or long inference times. Additionally, real-time endpoints incur continuous instance costs, which is not cost-effective for occasional large jobs.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless Inference has a maximum payload size of 6 MB and a maximum timeout of 60 seconds, making it unsuitable for 1 GB payloads or 10-minute inference times. It is designed for intermittent, short-lived inference requests, not for large, long-running jobs.
- ✓
SageMaker Asynchronous Inference
Why this is correct
Asynchronous Inference supports payloads up to 1 GB and allows inference times up to 15 minutes. It queues requests and processes them asynchronously, returning results via Amazon S3. This makes it ideal for large payloads and long-running inference, and it can scale to zero when idle, minimizing cost.
- ✗
SageMaker batch transform
Why it's wrong here
Batch transform is designed for offline, batch processing of entire datasets, not for individual requests that need to be processed on demand. While it can handle large payloads, it requires you to provide input data in S3 and is not suited for interactive or near-real-time use cases. It also does not provide an endpoint for ad-hoc requests.
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 |
Go deeper
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.