Designing-and-Implementing-a-Data-Science-Solution-on-Azure-(DP-100).pdf

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DP-100 DP-100 Dumps DP-100 Braindumps DP-100 Real Questions DP-100 Practice Test DP-100 dumps free Microsoft Designing and Implementing a Data Science Solution on Azure
Question: 114 ou use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model. You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module. Which two hyperparameters should you use? Each correct answer presents part of the solution. Each correct selection is worth one point. NOTE:
Question: 115 HOTSPOT - You are evaluating a Python NumPy array that contains six data points defined as follows: data = [10, 20, 30, 40, 50, 60] You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library: train: [10 40 50 60], test: [20 30] train: [20 30 40 60], test: [10 50] train: [10 20 30 50], test: [40 60] You need to implement a cross-validation to generate the output. How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area. Each correct selection is worth one point. NOTE: Hot Area:
Question: 116 You create a binary classification model by using Azure Machine Learning Studio. You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements: ? iterate all possible combinations of hyperparameters ? minimize computing resources required to perform the sweep You need to perform a parameter sweep of the model. Which parameter sweep mode should you use?
Question: 117
You are building a recurrent neural network to perform a binary classification. The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is overfitted. Which of the following is correct? A. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model. B. The training loss decreases while the validation loss increases when training the model. C. The training loss stays constant and the validation loss decreases when training the model.

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