HOTSPOT (Drag and Drop is not supported)You have a feature set containing the following numerical features: X, Y, and Z.The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: 0.859122 Box 2: a positively linear relationship+1 indicates a strong positive linear relationship-1 indicates a strong negative linear correlation0 denotes no linear relationship between the two variables.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation
DRAG DROP (Drag and Drop is not supported)You plan to explore demographic data for home ownership in various cities. The data is in a CSV file with the following format: age,city,income,home_owner21,Chicago,50000,035,Seattle,120000,123,Seattle,65000,045,Seattle,130000,118,Chicago,48000,0You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results. The experiment must log the following information:-the number of observations in the dataset-a box plot of income by home_owner-a dictionary containing the city names and the average income for each cityYou need to use the appropriate logging methods of the experiment's run object to log the required information.How should you complete the code? To answer, drag the appropriate code segments to the correct locations. Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
Box 1: log The number of observations in the dataset.run.log(name, value, description='')Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.Example: run.log("accuracy", 0.95)Box 2: log_image A box plot of income by home_owner.log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.Example: run.log_image("ROC", plot=plt)Box 3: log_table A dictionary containing the city names and the average income for each city. log_table: Log a dictionary object to the run with the given name.
You use the Azure Machine Learning service to create a tabular dataset named training_data. You plan to use this dataset in a training script.You create a variable that references the dataset using the following code: training_ds = workspace.datasets.get("training_data")You define an estimator to run the script.You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.Which property should you set?
Answer(s): B
Example:# Get the training datasetdiabetes_ds = ws.datasets.get("Diabetes Dataset")# Create an estimator that uses the remote computehyper_estimator = SKLearn(source_directory=experiment_folder, inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py')
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb
You register a file dataset named csv_folder that references a folder. The folder includes multiple comma-separated values (CSV) files in an Azure storage blob container.You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:You have the following code:You need to pass the dataset to ensure that the script can read the files it references.Which code segment should you insert to replace the code comment?
Example:from azureml.train.estimator import Estimatorscript_params = {# to mount files referenced by mnist dataset'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),'--regularization': 0.5}est = Estimator(source_directory=script_folder,script_params=script_params,compute_target=compute_target,environment_definition=env,entry_script='train.py')
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml
You are creating a new Azure Machine Learning pipeline using the designer.The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.Which module should you add to the pipeline in Designer?
Answer(s): C
The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a WebURL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.Example:from azureml.core import Datasetiris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')])
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline
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