Free Microsoft DP-100 Exam Questions

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You create a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script. Solution: Run the following code:

You are implementing hyperparameter tuning by using Bayesian sampling for a model training from a notebook. The notebook is in an Azure Machine Learning workspace that uses a compute cluster with 20 nodes. The code implements Bandit termination policy with slack factor set to 0.2 and the HyperDriveConfig class instance with max_concurrent_runs set to 10. You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?

You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data. You must use a Responsible AI dashboard to access the model. You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard. What should you do first?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are analyzing a numerical dataset which contains missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set. You need to analyze a full dataset to include all values. Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method. Does the solution meet the goal?

You create an Azure Machine Learning workspace. You must create a custom role named DataScientist that meets the following requirements: Role members must not be able to delete the workspace. Role members must not be able to create, update, or delete compute resource in the workspace. Role members must not be able to add new users to the workspace. You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace. The custom role must enforce the restrictions specified by the IT Operations team. Which JSON code segment should you use?