AWS Certified Machine Learning – Specialty — Question 279

A mining company wants to use machine learning (ML) models to identify mineral images in real time. A data science team built an image recognition model that is based on convolutional neural network (CNN). The team trained the model on Amazon SageMaker by using GPU instances. The team will deploy the model to a SageMaker endpoint.

The data science team already knows the workload traffic patterns. The team must determine instance type and configuration for the workloads.

Which solution will meet these requirements with the LEAST development effort?

Answer options

• A. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Default job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
• B. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Advanced job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.
• C. Deploy the model to an endpoint by using GPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.
• D. Deploy the model to an endpoint by using CPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.

Correct answer: B

Explanation

Amazon SageMaker Inference Recommender reduces development effort by automating load testing to find the best instance configuration. The Advanced job type is necessary because it allows the team to specify their custom, known traffic pattern for the load test, whereas the Default job type uses preconfigured settings. Options C and D require significant manual development effort to set up external load-testing tools and API integration.