A team has a SageMaker real-time endpoint serving a model. They want to deploy a new model version and gradually shift traffic to it, monitoring for errors. If the error rate exceeds a threshold, they want to automatically roll back to the previous version. Which deployment strategy should they use?

Canary deployment with automated rollback using CloudWatch alarms

Blue/green deployment with manual rollback

A/B testing with manual traffic shifting

Shadow deployment with traffic mirroring

A retail company needs to generate product recommendations for all customers in its loyalty program. The job runs nightly, processes millions of customer records, and the results are stored in Amazon S3 for the next day's website. The team wants to minimize cost and does not need real-time responses. Which inference method should they use?

SageMaker asynchronous endpoint

AWSCertified Machine Learning Engineer - Associate

Domain 3Objective 1

Task 3.1: Select Deployment Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 7)

Part of the Content Domain 3: Deployment and Orchestration of ML Workflows domain, which accounts for 22% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~11–19 in this domain), expect 4–6 from this objective — we provide 33 practice questions to prepare you well beyond it. (estimate)

33questions here

7free pages

13concepts

22%of the exam

13 Concepts in This Objective

Deployment best practices
AWS deployment services
Real-time and batch inference methods
Compute provisioning for production and test
Endpoint types and requirements
Container selection
Edge optimization with SageMaker Neo
Performance, cost, and latency tradeoffs
Compute environment selection for training and inference
Deployment orchestrators
Multi-model and multi-container deployments
Deployment targets
Model deployment strategies

Questions 31–33

31foundation · easy
A machine learning engineer needs to deploy a new version of a model to a SageMaker endpoint while keeping the current version available for immediate rollback if the new version performs poorly. Which deployment strategy directly supports this requirement?
Select an answer first
32expert · hard
A team has a SageMaker real-time endpoint serving a model. They want to deploy a new model version and gradually shift traffic to it, monitoring for errors. If the error rate exceeds a threshold, they want to automatically roll back to the previous version. Which deployment strategy should they use?
Select an answer first
33application · medium
A retail company needs to generate product recommendations for all customers in its loyalty program. The job runs nightly, processes millions of customer records, and the results are stored in Amazon S3 for the next day's website. The team wants to minimize cost and does not need real-time responses. Which inference method should they use?
Select an answer first

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Task 3.1: Select Deployment Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 7)

Questions 31–33

A machine learning engineer needs to deploy a new version of a model to a SageMaker endpoint while keeping the current version available for immediate rollback if the new version performs poorly. Which deployment strategy directly supports this requirement?

Keep studying — Content Domain 3: Deployment and Orchestration of ML Workflows