MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to deploy a trained XGBoost model for batch inference on a large dataset stored in S3. The inference job should be cost-effective and does not require real-time responses. 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 Batch Transform
SageMaker Batch Transform is designed for batch inference on large datasets stored in S3, processing data in chunks and writing results to S3. It is cost-effective for non-real-time scenarios. Real-time endpoints are for low-latency inference. Serverless is for on-demand, not batch. Asynchronous is for near-real-time with S3 input/output but still not ideal for large batch jobs.
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 this is correct
Batch Transform is designed for batch inference on S3 data, cost-effective and no real-time requirement.
- ✗
SageMaker real-time endpoint
Why it's wrong here
Real-time endpoints are for low-latency inference, not cost-effective for batch processing.
- ✗
SageMaker Asynchronous Inference
Why it's wrong here
Asynchronous is for near-real-time with S3 input, but less cost-effective for large batch jobs than Batch Transform.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless is for on-demand inference, not batch processing of large datasets.
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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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.