You work as a machine learning specialist for a car manufacturer that has developed driverless technology for their new line of cars. These cars require real-time machine learning models to perform all of the tasks of driving. You have trained multiple models, using different algorithms and/or different hyperparameters, as candidates to assist in lane line crossover detection using live data from sensors on the undercarriage of the car. You want to select one of these models as the model to go to production in the line of cars.
Using the various options available from SageMaker, which are the most effective method steps you should use to select the correct model? (Select TWO)

Answer options:

A.Use online testing with historical data.
B.Deploy your trained models to beta endpoints, then using a jupyter notebook in your SageMaker instance, send inference requests to each model, in turn, using the AWS SDK for python or the SageMaker high-level python library and finally evaluate each model.
C.Use online testing with live data.
D.Deploy your models to a SageMaker training instance, then train each model on a portion of the live data and finally evaluate each model.
E.Deploy your models to a SageMaker endpoint, then send a portion of the live data to each model and finally evaluate each model.

Answers: C, E
Option A is incorrect. For online testing, you use live data. For offline testing, you use historical data.
Option B is incorrect. When performing offline testing of your models, you deploy your trained models to alpha endpoints, not beta endpoints.
Option C is correct. For online testing, you use live data. Testing with live data will allow you to perform the steps listed in option
E.Option D is incorrect. To use online testing, you deploy your models to a SageMaker endpoint, not a SageMaker training instance.
Option E is correct. To perform online testing of your models, you deploy the models to a SageMaker endpoint and then send a portion of the data to each model (or production variant), allowing you to evaluate the models.
Reference:
Please see the SageMaker developer guide titled Validate a Machine Learning Model.