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MLA-C01 Practice Question: A model deployed on a SageMaker endpoint is…
A model deployed on a SageMaker endpoint is returning predictions. The team wants to log all predictions to an S3 bucket for auditing. What is the most efficient way to achieve this?
⚠ Common exam trap
The trap here is that candidates overcomplicate the solution by choosing a streaming or custom logging approach (like Kinesis or code modification), not realizing that SageMaker provides a built-in, zero-code feature (Data Capture) specifically designed for this auditing requirement.
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
✓
Enable SageMaker endpoint data capture to the S3 bucket.
SageMaker endpoint data capture is the native, most efficient way to log predictions to S3 because it automatically captures input payloads and output predictions for all requests to the endpoint, storing them directly in the specified S3 bucket without any custom code or additional infrastructure. This feature is designed specifically for auditing and monitoring, requiring only a DataCaptureConfig to be set on the endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable SageMaker endpoint data capture to the S3 bucket.
Why this is correct
Data capture is built-in and efficient.
- ✗
Configure CloudWatch Logs to export to S3.
Why it's wrong here
CloudWatch Logs is for logs, not prediction data.
- ✗
Modify the inference code to write logs to S3.
Why it's wrong here
Custom code requires maintenance and adds latency.
- ✗
Use Amazon Kinesis Data Firehose to stream predictions to S3.
Why it's wrong here
Firehose adds unnecessary complexity for simple logging.
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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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.