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.An IT department creates the following Azure resource groups and resources:The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace. Run the training script as an experiment on the aks- cluster compute target.Does the solution meet the goal?
Answer(s): B
Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
HOTSPOT (Drag and Drop is not supported)You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment:For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: No max_total_runs (50 here)The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value.Box 2: Yes Policy EarlyTerminationPolicy The early termination policy to use. If None - the default, no early termination policy will be used.Box 3: No Discrete hyperparameters are specified as a choice among discrete values. choice can be:- one or more comma-separated values- a range object- any arbitrary list object
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
HOTSPOT (Drag and Drop is not supported)You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script.You run the following Python code:For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: Yes The compute is created within your workspace region as a resource that can be shared with other users.Box 2: Yes It is displayed as a compute cluster.View compute targets 1. To see all compute targets for your workspace, use the following steps:2. Navigate to Azure Machine Learning studio.3. Under Manage, select Compute.4. Select tabs at the top to show each type of compute target.Box 3: Yes min_nodes is not specified, so it defaults to 0.
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
HOTSPOT (Drag and Drop is not supported)You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.You run the following code.For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: No FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.Box 2: Yes to_path() gets a list of file paths for each file stream defined by the dataset.Box 3: Yes TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.TabularDataset represents data in a tabular format created by parsing the provided file or list of files.Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset
You create a batch inference pipeline by using the Azure ML SDK. You configure the pipeline parameters by executing the following code:You need to obtain the output from the pipeline execution.Where will you find the output?
Answer(s): E
output_action (str): How the output is to be organized. Currently supported values are 'append_row' and 'summary_only'.'append_row' ג€" All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.'summary_only'
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig
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