Designing and Implementing a Data Science Solution on Azure — Question 176

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

What should you do?

Answer options

• A. Register a metric 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.
• B. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.
• C. 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.
• D. Specify a different name for the model each time you register it.

Correct answer: B

Explanation

The correct answer is B because specifying the model framework version allows for a systematic way to ensure that only improved models are registered, which is key for maintaining accuracy in the batch inference pipeline. Option A suggests using the accuracy metric as a tag, but does not align with the requirement of using framework versioning. Options C and D do not adhere to the requirement of only registering improved models based on accuracy.