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.You are using Azure Machine Learning Studio to perform feature engineering on a dataset.You need to normalize values to produce a feature column grouped into bins.Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.Does the solution meet the goal?
Answer(s): B
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
HOTSPOT (Drag and Drop is not supported)You are preparing to use the Azure ML SDK to run an experiment and need to create compute. 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:
Answer(s): A
Box 1: No If a compute cluster already exists it will be used.Box 2: Yes The wait_for_completion method waits for the current provisioning operation to finish on the cluster.Box 3: Yes Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.Box 4: No Need to use training_compute.delete() to deprovision and delete the AmlCompute target.
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget
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.You are a data scientist using Azure Machine Learning Studio.You need to normalize values to produce an output column into bins to predict a target column.Solution: Apply a Quantiles normalization with a QuantileIndex normalization.Does the solution meet the goal?
Use the Entropy MDL binning mode which has a target column.
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.You are creating a new experiment in Azure Machine Learning Studio.One class has a much smaller number of observations than the other classes in the training set.You need to select an appropriate data sampling strategy to compensate for the class imbalance.Solution: You use the Scale and Reduce sampling mode.Does the solution meet the goal?
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.Incorrect Answers:Common data tasks for the Scale and Reduce sampling mode include clipping, binning, and normalizing numerical values.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/data-transformation-scale-and-reduce
You are analyzing a dataset by using Azure Machine Learning Studio.You need to generate a statistical summary that contains the p-value and the unique count for each feature column.Which two modules can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
Answer(s): C,E
The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table (deprecated) and Count Featurizer(deprecated) modules.E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:- How many missing values are there in each column?- How many unique values are there in a feature column?- What is the mean and standard deviation for each column?- The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.Incorrect Answers:A: The Compute Linear Correlation module in Azure Machine Learning Studio is used to compute a set of Pearson correlation coefficients for each possible pair of variables in the input dataset.C: With Python, you can perform tasks that aren't currently supported by existing Studio modules such as:Visualizing data using matplotlibUsing Python libraries to enumerate datasets and models in your workspaceReading, loading, and manipulating data from sources not supported by the Import Data moduleD: The purpose of the Convert to Indicator Values module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-count-table https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data
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