hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company has a SageMaker endpoint running a…
A company has a SageMaker endpoint running a model that provides real-time recommendations. Recently, the model's accuracy has degraded due to data drift. The team wants to automatically retrain the model when a drift metric exceeds a threshold and deploy the new model without downtime. Which architecture should the team implement?
⚠ Common exam trap
AWS often tests the distinction between automatic drift-triggered retraining with zero-downtime deployment (Option B) versus scheduled retraining or manual intervention, and candidates may overlook the need to update the existing endpoint rather than creating a new one.
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
✓
Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant
It uses SageMaker Model Monitor to detect data drift and emit an EventBridge event, which triggers a SageMaker Pipeline to retrain the model, register it in the Model Registry, and then update the existing endpoint with a new production variant. This architecture enables automatic retraining and zero-downtime deployment by leveraging the endpoint's production variants for a blue/green deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Model Monitor to collect drift metrics, and have a data scientist manually analyze the metrics and trigger retraining via the SageMaker console
Why it's wrong here
Manual process contradicts automatic requirement.
- ✓
Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant
Why this is correct
EventBridge triggers pipeline on drift; pipeline retrains, registers, and uses production variant to shift traffic gradually with no downtime.
- ✗
Schedule a daily SageMaker Pipeline that retrains the model and deploys it using a new endpoint, then updates the application to point to the new endpoint
Why it's wrong here
Scheduled retraining doesn't react to drift; updating application endpoint may cause downtime if not careful.
- ✗
Use SageMaker Model Monitor to publish drift metrics to Amazon CloudWatch, and create a CloudWatch alarm that triggers an AWS Lambda function to retrain and deploy the model
Why it's wrong here
Lambda can trigger retraining, but deploying a new model with no downtime requires more orchestration; Lambda may not handle complex pipeline.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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