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NEW QUESTION 42 You use the Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline. You must deploy the real-time inference pipeline as a web service. What must you do before you deploy the real-time inference pipeline?

• A. Create a batch inference pipeline.
• B. Clone the training pipeline.
• C. Run the real-time inference pipeline.
• D. Create an Azure Machine Learning compute cluster.

Answer: D Explanation: You need to create an inferencing cluster. Deploy the real-time endpoint After your AKS service has finished provisioning, return to the real-time inferencing pipeline to complete deployment. Select Deploy above the canvas. Select Deploy new real-time endpoint. Select the AKS cluster you created. Select Deploy. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy   NEW QUESTION 43 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 Synthetic Minority Oversampling Technique (SMOTE) sampling mode. Does the solution meet the goal?

• A. No
• B. Yes

Answer: B Explanation: 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. References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote   NEW QUESTION 44 You are performing sentiment analysis using a CSV file that includes 12.0O0 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and Configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset. You must create a new n-gram text dictionary from the customer review text and set the maximum n-gram size to trigrams. You need to configure the Extract N Gram features from Text module. What should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Answer: ** Explanation:   **NEW QUESTION 45 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.

• A. Replace the training dataset module with a data input that does not include the price column.
• B. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
• C. Remove the Apply SQL Transformation module from the data flow.
• D. Add a Select Columns module before the Score Model module to select all columns other than price.
• E. Replace the Web Service Input module with a data input that does not include the price column.
• F. Connect the output of the Apply SQL Transformation to the Web Service Output module.

Answer: B,D,F   NEW QUESTION 46 ...... DOWNLOAD the newest PassTestking DP-100 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=19GM9HLA3YrLe-_nnOGAGZ2OFGQIQIpRQ