Question 973 of 1,672
SageMaker Blue/Green and Canary Deployment
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.)
Quick Answer
The answer is blue/green and canary deployment. Both strategies achieve zero-downtime updates by routing traffic gradually between an existing production endpoint and a new endpoint hosting the updated model, ensuring no requests are dropped during the transition. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s deployment modes and the critical distinction that you cannot update a model in-place on a live endpoint—you must create a new endpoint configuration. A common trap is choosing “rolling update” or “direct model update,” which SageMaker does not support natively, or “delete and recreate,” which causes downtime. For the exam, remember that any option involving immediate traffic switching or in-place model changes is wrong; zero-downtime always requires a separate new endpoint. Memory tip: think “two endpoints, one traffic shift”—blue/green swaps all at once, canary does it gradually, but both keep the old endpoint alive until the new one is fully validated.
⚠ Common exam trap
Test-takers frequently confuse the concept of 'updating' a model with the ability to directly modify an existing endpoint, but SageMaker requires immutable deployments, and only traffic-shifting strategies like blue/green or canary deployments achieve zero-downtime updates.
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 blue/green deployment with endpoint variants.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Update the model artifact on the existing endpoint.
Why it's wrong here
Not supported.
- ✗
Delete the existing endpoint and create a new one.
Why it's wrong here
Causes downtime.
- ✓
Use blue/green deployment with endpoint variants.
Why this is correct
Traffic is shifted gradually.
- ✗
Use rolling update with multiple instances.
Why it's wrong here
SageMaker does not support rolling updates natively.
- ✓
Use canary deployment by gradually shifting traffic.
Why this is correct
Zero-downtime update.
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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 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?
hard- A.Use a rolling update strategy.
- B.Use a multi-model endpoint.
- C.Use Amazon SageMaker A/B testing.
- ✓ D.Use Amazon SageMaker canary deployment.
- ✓ E.Use Amazon SageMaker blue/green deployment.
Why D: 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.
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.
Last reviewed: Jul 4, 2026
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.
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