AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 125

An ML engineer needs to deploy four ML models in an Amazon SageMaker inference pipeline. The models were built with different frameworks. The ML engineer also needs to give clients the ability to use the invoke_endpoint call to perform inference for each model.

Which solution will meet these requirements MOST cost-effectively?

Answer options

• A. Create a SageMaker multi-model endpoint.
• B. Create a SageMaker multi-container endpoint.
• C. Create multiple SageMaker single-model endpoints.
• D. Run a SparkML job to generate multiple endpoints.

Correct answer: B

Explanation

The correct answer is B because a SageMaker multi-container endpoint allows the deployment of multiple models built on different frameworks in a single endpoint, which is cost-effective. Option A, a multi-model endpoint, is not suitable here as it typically requires models to be of the same framework. Option C involves creating separate endpoints for each model, which can be more expensive and less efficient. Option D does not directly relate to deploying models in SageMaker but rather suggests running a job that may not be needed for this scenario.