What are two possible ways to achieve this goal?

You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.

The model will be retrained each month as new data is available.

You must register the model for use in a batch inference pipeline.

You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A . Specify a different name for the model each time you register it.
B . Register the model with the same name each time regardless of accuracy, and always use the latest
version of the model in the batch inferencing pipeline.
C . Specify the model framework version when registering the model, and only register subsequent models if this value is higher.
D . Specify a property named accuracy with the accuracy metric as a value when
registering the model, and only register subsequent models if their accuracy is higher than
the accuracy property value of the
currently registered model.
E . Specify a tag named accuracy with the accuracy metric as a value when registering the
model, and only register subsequent models if their accuracy is higher than the accuracy
tag value of the currently
registered model.

View Answer

Answer: C,E

Explanation:

E: Using tags, you can track useful information such as the name and version of the

machine learning library used to train the model. Note that tags must be alphanumeric.

Reference: https://notebooks.azure.com/xavierheriat/projects/azureml-getting-started/html/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-

image-deploy-service.ipynb