MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer deploys a new model version to a SageMaker endpoint with production variants. They want to gradually shift traffic from the old model to the new model, monitoring for errors, and automatically roll back if the error rate exceeds 5%. Which deployment pattern should they use?
⚠ Common exam trap
MLA-C01 often tests the distinction between canary (gradual shift + auto rollback) and A/B testing (statistical comparison) — candidates pick A/B testing because both involve traffic splitting, but only canary is designed for progressive rollout with automated rollback on error thresholds.
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
✓
Canary deployment with CloudWatch alarms
Canary deployment with CloudWatch alarms is correct because SageMaker production variants allow you to split traffic between the old and new model (e.g., 90/10), and CloudWatch alarms can monitor the new variant's error rate and trigger automatic rollback when it exceeds 5%. This pattern is purpose-built for gradual, monitored traffic shifting with automated safety nets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Canary deployment with CloudWatch alarms
Why this is correct
A canary deployment routes a small percentage of traffic to the new variant while CloudWatch alarms watch the error rate, triggering automatic rollback when it exceeds 5%. It satisfies the stem's gradual-shift and auto-rollback constraints, unlike all-at-once or shadow patterns.
- ✗
A/B testing with traffic splitting
Why it's wrong here
A/B testing splits traffic between variants for comparison but does not itself shift weights progressively or trigger rollback on error thresholds. It suits experiments measuring business metrics, whereas linear or canary deployment with CloudWatch alarms performs the gradual shift and automatic rollback described.
- ✗
Blue/green deployment
Why it's wrong here
Blue/green deployment shifts all traffic at once after the green environment passes health checks, so it cannot gradually ramp traffic or trigger rollback on a 5% error-rate threshold. It suits zero-downtime cutovers with instant full switchover, not incremental canary monitoring.
- ✗
Shadow testing
Why it's wrong here
Shadow testing mirrors live traffic to the new model without serving its responses, so no traffic is actually shifted and no rollback on error rate can occur. It is tempting because it safely compares model predictions against production, which suits pre-release validation where you want performance data before any user impact.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.