mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning team is deploying a fraud…
A machine learning team is deploying a fraud detection model using SageMaker. They use the SageMaker Model Registry to track model versions. They want to automatically deploy the latest approved model to a production endpoint whenever a new model version is approved. The team uses a CI/CD pipeline with AWS CodePipeline. The pipeline currently includes a source stage (S3), a build stage (CodeBuild), and a deploy stage (manual approval). They want to automate the deployment of approved models. Which solution will meet these requirements with the least operational overhead?
⚠ Common exam trap
AWS often tests the misconception that you must build a custom Lambda or pipeline action to integrate SageMaker Model Registry with CodePipeline, when in fact EventBridge provides a native, low-overhead solution for event-driven pipeline triggers.
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
✓
Configure an EventBridge rule to trigger a CodePipeline execution when the model approval status changes.
It directly integrates SageMaker Model Registry approval events with CodePipeline via EventBridge, enabling fully automated deployment of the latest approved model to a production endpoint with minimal operational overhead. This approach avoids custom code or additional pipeline stages, leveraging native AWS event-driven architecture to trigger the pipeline only when a model version is approved.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a custom action to CodePipeline that uses a SageMaker deployment step.
Why it's wrong here
Custom actions require development and maintenance, increasing overhead.
- ✗
Create a Lambda function that triggers on Model Registry approval events and updates the endpoint using the boto3 SDK.
Why it's wrong here
Custom Lambda adds operational overhead and duplicates pipeline functionality.
- ✓
Configure an EventBridge rule to trigger a CodePipeline execution when the model approval status changes.
Why this is correct
EventBridge natively integrates with Model Registry events and triggers the pipeline automatically.
- ✗
Use SageMaker Pipelines to deploy the model directly upon training completion.
Why it's wrong here
This bypasses the approval process and existing pipeline.
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
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 835-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.