MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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.
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.
- ✗
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.
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 |
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Same concept, more angles
1 more way this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company needs to deploy a model that processes large payloads (up to 1 GB) asynchronously. The results should be written to S3, and the team needs SNS notifications upon completion. Which SageMaker inference option is MOST suitable?
easy- ✓ A.Asynchronous Inference
- B.Batch Transform
- C.Real-time endpoint
- D.Serverless Inference
Why A: Asynchronous Inference is designed for large payloads, processes requests asynchronously, and supports SNS notifications on completion.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.