hardMultiple Choice
MLA-C01 Practice Question: A company deploys a machine learning model as a…
A company deploys a machine learning model as a SageMaker real-time endpoint. They need to implement a mechanism to automatically roll back to the previous model version if performance degrades after a deployment. Which approach should they use?
⚠ Common exam trap
Test-takers frequently confuse manual rollback (Option A) as acceptable automation, or they overcomplicate the solution with external services like Route 53 (Option C) or CodeDeploy (Option D), missing that SageMaker's native deployment configuration with CloudWatch alarms provides a fully automated, integrated rollback mechanism.
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 the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback
SageMaker endpoints support deployment with traffic shifting (e.g., canary or linear patterns) via the 'DeploymentConfig' parameter, and you can attach CloudWatch alarms to the endpoint's variant metrics. If the alarm triggers (e.g., due to increased error rate or latency), SageMaker automatically rolls back the traffic to the previous model version, ensuring minimal manual intervention and fast recovery.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually update the endpoint to point to the previous model version
Why it's wrong here
Manual rollback requires a human to detect degradation and intervene, so it cannot satisfy the automatic requirement. It is tempting because updating an endpoint to a previous model version is a genuine rollback action, and it would be the right choice when an operator deliberately reverts after investigating an incident.
- ✓
Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback
Why this is correct
Traffic shifting with CloudWatch alarms enables automatic rollback: alarms on endpoint metrics trigger the deployment to revert traffic to the previous model version. This satisfies the requirement for automatic rollback on performance degradation without manual intervention.
- ✗
Create multiple endpoints and use Amazon Route 53 weighted routing to shift traffic
Why it's wrong here
Route 53 weighted routing shifts traffic between separate endpoints but provides no health evaluation of model performance, so no automatic rollback triggers. It is tempting because weighted DNS routing genuinely supports gradual canary traffic shifting, and it would be correct when distributing load across independent, already-validated endpoints.
- ✗
Use AWS CodeDeploy with Amazon EC2 instances behind an Elastic Load Balancer
Why it's wrong here
CodeDeploy with EC2 behind a load balancer manages instance deployments, not SageMaker endpoint variant weights, so it cannot roll back the model. It is tempting because CodeDeploy does provide automatic rollback on CloudWatch alarms, and it would be correct for blue/green deployments of application code on EC2.
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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.