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NEW QUESTION: 1
Given the following formula:
Case (Children > 1 and Bedrooms = Children; "Overcrowded" ;
Children > 2 and Bedrooms = Children; "Crowded" ;
Children < 4 or Bedrooms > 1; "Comfortable"
)
Assuming the value of Children is 3 and the value of Bedrooms is 3, what is the result?
A. Crowded
B. Comfortable
C. Overcrowded Crowded Comfortable
D. Returns no result
E. Overcrowded
Answer: E

NEW QUESTION: 2
You want to have a NetApp FlexVol that can both grow and shrink dynamically as required by current capacity needs. Which settings will enable this feature?
A. This can be enabled with the inline-compression volume efficiency option.
B. This can be enabled with the modify volume efficiency option.
C. This can be enabled with the grow volume autosize mode.
D. This can beenabled with the grow_shrink volume autosize mode
Answer: D

NEW QUESTION: 3
You are building a classification experiment in Azure Machine Learning.
You need to ensure that you can use the Evaluate Model module in the experiment.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Connect the output of the Score Model modules to the input of the Evaluate Model module.
B. Connect the output of the Score Model modules to the input of the Train Model modules and the input of the Split Data modules.
C. Connect the input of the Score Model modules to the output of the Train Model modules and the output Split Data modules.
D. Connect the input of the Score Model modules to the output of the Evaluate Model module.
Answer: A,C
Explanation:
Explanation/Reference:
Explanation:
Use the training data.
To evaluate a model, you must connect a dataset that contains a set of input columns and scores. If no other data is available, you can use your original dataset.
1. Connect the Scored dataset output of the Score Model to the input of Evaluate Model.
2. Click Evaluate Model module, and select Run selected to generate the evaluation scores.
Use testing data
A common scenario in machine learning is to separate your original data set into training and testing datasets, using the Split module, or the Partition and Sample module.
1. Connect the Scored dataset output of the Score Model to the input of Evaluate Model.
2. Connect the output of the Split Data module that contains the testing data to the right-hand input of Evaluate Model.
3. Click Evaluate Model module, and select Run selected to generate the evaluation scores.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate- model