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MLA-C01 Practice Question: A company uses SageMaker for training and…
A company uses SageMaker for training and inference. They have a model that retrains weekly. After each retraining, the model is evaluated on a held-out test set. If the evaluation metrics meet a threshold, the model is registered as 'Approved' in the SageMaker Model Registry. The team manually deploys the approved model to a production endpoint. They want to automate this deployment process to reduce manual errors. However, the deployment should only proceed if the new model passes a canary test in a staging environment. Which combination of AWS services should the team use to achieve this?
⚠ Common exam trap
The trap here is that candidates may overthink the solution and choose a generic CI/CD tool like CodeDeploy or Lambda, missing that SageMaker Pipelines already provides a fully managed, ML-specific orchestration with conditional deployment and canary testing capabilities.
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
✓
SageMaker Pipelines with a conditional deployment step that includes a canary test.
SageMaker Pipelines natively supports conditional execution steps, allowing you to add a canary test step that evaluates the new model in a staging environment before automatically promoting it to production. This directly addresses the requirement for automated deployment gated by a canary test, without needing external orchestration services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS CodeDeploy with a blue/green deployment strategy.
Why it's wrong here
CodeDeploy is designed for EC2/ECS/Lambda deployments, not SageMaker endpoints.
- ✓
SageMaker Pipelines with a conditional deployment step that includes a canary test.
Why this is correct
Pipelines natively support conditional logic, canary deployments via weighted endpoints, and automatic rollback.
- ✗
AWS Lambda to deploy to staging, then automatically promote to production if staging tests pass.
Why it's wrong here
Lambda requires custom code for canary testing and promotion, increasing complexity.
- ✗
Amazon EKS with a custom inference container and use ArgoCD for automated deployments.
Why it's wrong here
This approach moves away from SageMaker managed service, adding significant operational overhead.
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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.