DP-100 – Designing and Implementing a Data Science Solution on Azure

DRAG DROP

You are building an intelligent solution using machine learning models.

The environment must support the following requirements:

Data scientists must build notebooks in a cloud environment

Data scientists must use automatic feature engineering and model building in machine learning pipelines.

Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation. Notebooks must be exportable to be version controlled locally.

You need to create the environment.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Select and Place:

You create an Azure Machine Learning workspace.

You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.

You need to implement the compute resources.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You have a binary classifier that predicts positive cases of diabetes within two separate age groups.

The classifier exhibits a high degree of disparity between the age groups.

You need to modify the output of the classifier to maximize its degree of fairness across the age groups and meet the following requirements:

• Eliminate the need to retrain the model on which the classifier is based.

• Minimize the disparity between true positive rates and false positive rates across age groups.

Which algorithm and panty constraint should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.