MLS-C01 Question #354: Real Exam Question with Answer & Explanation

Question

An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to make changes to applications that rely on the API. The company also wants to evaluate the performance of the new model in production traffic before the company fully rolls out the new model to all users. Which solution will meet these requirements with the LEAST operational overhead?

Options

• ACreate a new SageMaker endpoint for the new model. Configure an Application Load Balancer
• BModify the existing endpoint to use SageMaker production variants to distribute traffic between
• CModify the existing endpoint to use SageMaker batch transform to distribute traffic between the
• DCreate a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB)

Explanation

To update a SageMaker real-time ML recommendation engine API with a new model, evaluate its performance with production traffic, and avoid application changes with minimal operational overhead, SageMaker production variants are the ideal solution.

Common mistakes.

• A. Creating a new SageMaker endpoint for the new model would require applications relying on the API to update their endpoint URL, violating the requirement to avoid changes to applications.
• C. Amazon SageMaker batch transform is designed for offline, asynchronous inference on large datasets, not for real-time, low-latency API predictions needed for a recommendation engine.
• D. Creating a new SageMaker endpoint for the new model, similar to option A, would require changes to dependent applications, failing to meet the specified requirement.

Concept tested. SageMaker A/B testing and model deployment

Reference. https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html

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