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NEW QUESTION 46
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
NEW QUESTION 47
You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.
You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement.
What should you do?
- A. Delete the Web Service Input module from the inference pipeline.
- B. Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.
- C. Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.
- D. Delete the dataset from the training pipeline and recreate the real-time inference pipeline.
Answer: C
Explanation:
By default, the Web Service Input will expect the same data schema as the module output data which connects to the same downstream port as it. You can remove the target variable column in the inference pipeline using Select Columns in Dataset module. Make sure that the output of Select Columns in Dataset removing target variable column is connected to the same port as the output of the Web Service Intput module.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy
NEW QUESTION 48
You plan to use .a Deep learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the IXVM to support CUOA
What should you implement?
- A. Graphic Processing Unit (GPU)
- B. Intel Software Guard Extensions (Intel SGX) technology
- C. Computer Processing Unit (CPU) speed increase by using overcloking
- D. Solid State Drives (SSD)
- E. High Random Access Memory (RAM) configuration
Answer: D
NEW QUESTION 49
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