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Download Designing and Implementing a Data Science Solution on Azure Exam Dumps

NEW QUESTION 28
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. Run the real-time inference pipeline.
• C. Clone the training 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 29
You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module.
Which splitting mode should you use?

• A. Split Rows with the Randomized split parameter set to true
• B. Relative Expression Split
• C. Regular Expression Split
• D. Recommender Split

Answer: A

NEW QUESTION 30
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?

• A. Filter Based Feature Selection
• B. Permutation Feature Importance
• C. Synthetic Minority Oversampling Technique (SMOTE)
• D. Fisher Linear Discriminant Analysis.

Answer: C

Explanation:
Use the SMOTE module in Azure Machine Learning Studio (classic) 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.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION 31
You need to select a feature extraction method.
Which method should you use?

• A. Fisher Linear Discriminant Analysis
• B. Spearman correlation
• C. Pearson's correlation
• D. Mutual information

Answer: B

Explanation:
Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Incorrect Answers:
B: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not).
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules Perform Feature Engineering Question Set 3

NEW QUESTION 32
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 first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using 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:

NEW QUESTION 33
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