Minimizing Downtime with Canary and Blue/Green Deployments
A company is deploying a machine learning model on Amazon SageMaker. The model needs to be updated frequently with new versions. The team wants to minimize downtime and test the new model version before routing all traffic to it. Which TWO strategies should be used together?
Quick Answer
The scenario asks for two things at once, minimal downtime and the ability to validate a new model version before it takes over all traffic, and blue/green and canary deployment are the two SageMaker mechanisms built specifically to satisfy both. Blue/green deployment stands up the new model version as a separate, fully running endpoint (or endpoint variant) alongside the existing one, so the old version keeps serving traffic uninterrupted while the new version is validated, which is what eliminates downtime during the switch. Canary deployment complements this by controlling how traffic moves between the two versions: rather than cutting over all at once, it routes a small percentage of live traffic to the new version first, letting the team observe real-world behavior before gradually increasing that share. The distractors misname or misapply SageMaker concepts: rolling updates aren't how SageMaker manages endpoint traffic, since it works through production variants instead; a multi-model endpoint is about hosting several distinct models on one endpoint to save cost, not about shifting traffic during an update; and the gradual-traffic-shifting behavior described here is specifically what SageMaker calls canary deployment, not generic A/B testing. When you see requirements for both safe validation and zero downtime during a model update, expect the answer to combine a parallel-environment strategy with a gradual traffic-shifting strategy.
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 Amazon SageMaker canary deployment.
The correct answers are D (canary deployment) and E (blue/green deployment). In Amazon SageMaker, blue/green deployment allows you to deploy a new model version alongside the existing one (blue) and then shift traffic gradually. Canary deployment is a feature of SageMaker that routes a small percentage of traffic to the new version for testing before shifting more. Together, these strategies minimize downtime and allow testing. Option A (rolling update) is not directly supported in SageMaker for endpoints; SageMaker uses deployment variants. Option B (multi-model endpoint) is for hosting multiple models on the same endpoint but does not provide traffic shifting for updates. Option C (A/B testing) in SageMaker is typically achieved using production variants with traffic weights, but the specific feature for gradual traffic shifting is called canary deployment, so option C is incorrect as stated.
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 a rolling update strategy.
Why it's wrong here
Rolling updates are not natively supported by SageMaker endpoints.
- ✗
Use a multi-model endpoint.
Why it's wrong here
Multi-model endpoints host multiple models but do not provide deployment strategies.
- ✗
Use Amazon SageMaker A/B testing.
Why it's wrong here
A/B testing is for comparing model versions, not specifically for traffic shifting with canary.
- ✓
Use Amazon SageMaker canary deployment.
Why this is correct
Canary deployment sends a small percentage of traffic to the new version.
- ✓
Use Amazon SageMaker blue/green deployment.
Why this is correct
Blue/green deploys a new endpoint and shifts traffic after validation.
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Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is deploying a machine learning model using Amazon SageMaker. The model must be updated frequently without downtime. Which TWO strategies can achieve this? (Choose two.)
hard- A.Update the model artifact on the existing endpoint.
- B.Delete the existing endpoint and create a new one.
- ✓ C.Use blue/green deployment with endpoint variants.
- D.Use rolling update with multiple instances.
- ✓ E.Use canary deployment by gradually shifting traffic.
Why C: Amazon SageMaker supports blue/green deployment using endpoint variants, where you can deploy a new model version alongside the current one and then shift all traffic to the new variant once validated. This approach ensures zero downtime because the existing endpoint remains active during the transition, and traffic is switched atomically. Option E is correct because canary deployment with SageMaker allows you to gradually shift a small percentage of traffic to a new model variant, monitor its performance, and then ramp up to 100% if successful, all without interrupting the service.
Variation 2. A company is deploying a machine learning model using Amazon SageMaker. The model needs to be updated frequently with new data. Which TWO approaches can be used to update the model without downtime? (Choose TWO.)
medium- A.Delete the existing endpoint and create a new one with the updated model.
- B.Directly update the model artifact in the existing endpoint configuration.
- ✓ C.Use SageMaker A/B testing to gradually shift traffic to the new model variant.
- D.Stop the endpoint, update the model, and restart the endpoint.
- ✓ E.Use a blue/green deployment by deploying the new model on a separate endpoint and then updating the DNS record.
Why C: Amazon SageMaker supports deploying multiple model variants behind a single endpoint using production variants. By using A/B testing (traffic shifting), you can gradually route a percentage of inference requests to the new model variant while the old variant continues serving the majority of traffic, enabling updates with zero downtime.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.