Exam DP-100 topic 4 question 59 discussion

Actual exam question from Microsoft's DP-100

Question #: 59
Topic #: 4

You manage an Azure Machine Learning workspace.

You build a custom model you must log with MLflow. The custom model includes the following:

• The model is not natively supported by MLflow.
• The model cannot be serialized in Pickle format.
• The model source code is complex.
• The Python library for the model must be packaged with the model.

You need to create a custom model flavor to enable logging with MLflow.

What should you use?

A. model loader
B. artifacts
C. model wrapper
D. custom signatures

Show Suggested Answer

Suggested Answer: A 🗳️

by damaldon at July 7, 2023, 6:34 p.m.

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evangelist

1 year, 1 month ago

Selected Answer: A

A is correct

upvoted 1 times

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kay1101

1 year, 2 months ago

Selected Answer: A

A. model loader reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2&tabs=loader#logging-custom-models

upvoted 1 times

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BR_CS

1 year, 11 months ago

Selected Answer: A

See comments by phdykd and damaldon. A is correct.

upvoted 3 times

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phdykd

2 years ago

A. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2&tabs=loader

upvoted 1 times

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damaldon

2 years ago

Ans. A Sometimes your model logic is complex and there are several source files that your model loads on inference time. This would be the case when you have a Python library for your model for instance. In this scenario, you want to package the library all along with your model so it can move as a single piece. Use this method when: Your model can't be serialized in Pickle format or there is a better format available for that. Your model artifacts can be stored in a folder where all the requiered artifacts are placed. Your model source code is complex and it requires multiple Python files. Potentially, there is a library that supports your model. You want to customize the way the model is loaded and how the predict function works. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2&tabs=loader

upvoted 2 times

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