hardMultiple Choice
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 analysis and console-triggered retraining cannot satisfy the automatic, threshold-driven requirement, and console deployment risks endpoint downtime. It is tempting because Model Monitor genuinely detects drift, and manual review would be correct for low-volume models where a data scientist validates each retrain before release.
- ✓
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
Model Monitor detects drift and emits CloudWatch metrics; EventBridge rules trigger a SageMaker Pipeline that retrains, registers the model, and performs a variant update. Updating the endpoint with a new production variant shifts traffic without downtime, satisfying both automatic retraining and zero-downtime deployment.
- ✗
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
A daily schedule retrains regardless of drift, so the threshold trigger is ignored, and repointing the application after creating a new endpoint introduces downtime rather than avoiding it. SageMaker Pipelines suit scheduled retraining workflows, but this scenario demands event-driven retraining with in-place endpoint updates.
- ✗
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 alone cannot retrain and redeploy a SageMaker model reliably; the architecture omits SageMaker Pipelines or a training job plus endpoint update, so no automated retraining occurs. It is tempting because CloudWatch alarms and Lambda are the standard drift-trigger pattern, but the retraining and deployment steps must be defined.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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