The answer is to configure an EventBridge rule that triggers a CodePipeline execution when the model approval status changes. This solution is correct because it directly connects SageMaker Model Registry approval events to the CI/CD pipeline using a native event-driven architecture, eliminating the need for custom polling scripts or additional pipeline stages. When a model version transitions to "Approved," EventBridge automatically invokes CodePipeline, which then runs the deploy stage to push the latest approved model to the production endpoint. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of integrating SageMaker Model Registry with event-driven automation, often appearing as a trap where candidates overcomplicate the solution with Lambda functions or manual approval steps. The key insight is that EventBridge serves as the glue between model approval and pipeline execution, minimizing operational overhead. Memory tip: think "EventBridge bridges approval to deployment" — no extra code needed.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Pipelines to deploy the model directly upon training completion.
Why it's wrong here
This bypasses the approval process and existing pipeline.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
EventBridge can capture SageMaker Model Registry state changes (e.g., ApprovalStatus changed to Approved) via the 'SageMaker Model Package State Change' event, and target a CodePipeline execution. This eliminates the need for polling or custom triggers, and the pipeline can then use a deploy stage (e.g., with AWS CloudFormation or SageMaker SDK) to update the endpoint. Under the hood, EventBridge uses a rule with an event pattern matching the specific model package group and approval status, ensuring only approved versions trigger the pipeline.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure an EventBridge rule to trigger a CodePipeline execution when the model approval status changes. — Option C is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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