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:
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 MimicExplainer.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 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?
You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework.Which of the following DSVM should you create?
You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code: from azureml.pipeline.core import Pipeline from azureml.core.experiment import Experiment pipeline = Pipeline(workspace=ws, steps=[parallelrun_step]) pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)You need to monitor the progress of the pipeline execution.What are two possible ways to achieve this goal? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
You need to implement a scaling strategy for the local penalty detection data.Which normalization type should you use?
