hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A financial services company is deploying a…
A financial services company is deploying a credit risk model using SageMaker. They require that the model always uses the latest approved version from the Model Registry. They also need to maintain a detailed audit trail of all model version transitions (e.g., from PendingApproval to Approved). The deployment should be fully automated and must roll back immediately if the new model's error rate exceeds the old model's error rate by more than 2% during a canary deployment. Which solution meets these requirements with the least custom code?
⚠ Common exam trap
The trap here is that candidates may overcomplicate the solution by choosing custom Lambda or Kubernetes options, missing that SageMaker Pipelines provides a fully managed, code-minimal way to orchestrate canary deployments with automated rollback via CloudWatch alarms.
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 Pipelines with a conditional step to deploy the model after approval, and include a canary deployment using a weight endpoint variant. Use CloudWatch alarms to trigger automatic rollback.
SageMaker Pipelines natively supports conditional execution and canary deployments using endpoint weight variants, which together enable automated rollback triggered by CloudWatch alarms when the error rate exceeds the 2% threshold. This approach requires minimal custom code by leveraging built-in SageMaker capabilities for model registry integration, deployment, and monitoring.
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 AWS CodePipeline with a deployment action that uses AWS CloudFormation to update the endpoint. Add a manual approval step for rollback.
Why it's wrong here
Manual rollback does not meet automatic rollback requirement, and CloudFormation adds overhead.
- ✓
Use SageMaker Pipelines with a conditional step to deploy the model after approval, and include a canary deployment using a weight endpoint variant. Use CloudWatch alarms to trigger automatic rollback.
Why this is correct
Pipelines natively integrate with Model Registry, conditional logic, and CloudWatch for automated canary and rollback.
- ✗
Create an AWS Lambda function that is triggered by Model Registry events, deploys the model to a staging endpoint, runs a canary test, and if successful, updates the production endpoint.
Why it's wrong here
Lambda requires custom code for staging, canary, and promotion, adding complexity.
- ✗
Use an Amazon EKS cluster with a custom inference container and use ArgoCD for automated deployments.
Why it's wrong here
EKS and ArgoCD introduce significant complexity and deviate from SageMaker managed services.
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.