DRAG DROP (Drag and Drop is not supported)You are building an experiment using the Azure Machine Learning designer.You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.You need to determine the Area Under the Curve (AUC) of the model.Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.Select and Place:
Answer(s): A
Step 1: Train Model Two-Class Boosted Decision Tree First, set up the boosted decision tree model.1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of theTrain Model module.The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).This portion of the experiment now looks something like this:Step 2: Score Model Score and evaluate the models You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.Add the Score Model modules 1. Find the Score Model module and drag it onto the canvas.2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.Step 3: Evaluate Model To evaluate the two scoring results and compare them, you use an Evaluate Model module.1. Find the Evaluate Model module and drag it onto the canvas.2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.3. Connect the other Score Model module to the right input port.
You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch1.3 framework to train the model.You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.You need to define the estimator that will be used to run the script.Which estimator type should you use?
Answer(s): B
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models
You create a pipeline in designer to train a model that predicts automobile prices.Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)Training pipeline You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.)Real-time pipeline You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): A,C,E
HOTSPOT (Drag and Drop is not supported)You register the following versions of a model.You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.After the run has been submitted and completed, 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:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where
You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure MachineLearning by using automated machine learning.The training dataset that you are using is highly unbalanced.You need to evaluate the classification model.Which primary metric should you use?
AUC_weighted is a Classification metric.Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.Incorrect Answers:A: normalized_mean_absolute_error is a regression metric, not a classification metric.C: When comparing approaches to imbalanced classification problems, consider using metrics beyond accuracy such as recall, precision, and AUROC. It may be that switching the metric you optimize for during parameter selection or model selection is enough to provide desirable performance detecting the minority class.D: normalized_root_mean_squared_error is a regression metric, not a classification metric.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
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