You write five Python scripts that must be processed in the order specified in Exhibit A `" which allows the same modules to run in parallel, but will wait for modules with dependencies.You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.You need to create the pipeline shown. Assume all relevant imports have been done.Which Python code segment should you use?
Answer(s): A
The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step
You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:You have the following script.You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.Which code should you use to configure the estimator?
Answer(s): B
Besides passing the dataset through the input parameters in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.Example:from azureml.train.sklearn import SKLearnscript_params = {# mount the dataset on the remote compute and pass the mounted path as an argument to the training script'--data-folder': mnist_ds.as_named_input('mnist').as_mount(),'--regularization': 0.5}est = SKLearn(source_directory=script_folder,script_params=script_params,compute_target=compute_target,environment_definition=env,entry_script='train_mnist.py')# Run the experimentrun = experiment.submit(est)run.wait_for_completion(show_output=True)Incorrect Answers:A: Pandas DataFrame not used.
https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets
DRAG DROP (Drag and Drop is not supported)You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.Which three pipeline steps should you run 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:
Step 1: Configure a DataTransferStep() to fetch new image dataג€¦Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch
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: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and runPython code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.Does the solution meet the goal?
Use the VM as a compute target.Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
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 and then run the training script as an experiment on local compute.Does the solution meet the goal?
Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
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