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 using Azure Machine Learning to run an experiment that trains a classification model.You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.Solution: Run the following code:Does the solution meet the goal?
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 train a classification model by using a logistic regression algorithm.You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.You need to create an explainer that you can use to retrieve the required global and local feature importance values.Solution: Create a TabularExplainer.Does the solution meet the goal?
You train and register a model in your Azure Machine Learning workspace.You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.You need to create the inferencing script for the ParallelRunStep pipeline step.Which two functions should you include? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
You need to implement a feature engineering strategy for the crowd sentiment local models.What should you do?
This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.You are preparing to create a virtual machine that has the necessary tools built into it.You need to make use of the correct virtual machine type.Recommendation: You make use of a Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition.Will the requirements be satisfied?
You need to implement a scaling strategy for the local penalty detection data.Which normalization type should you use?
