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NEW QUESTION: 1
You are completing the installation of the Data Quality Server component of SQL Server
Data Quality Services (DQS).
You need to complete the post-installation configuration.
What should you do?
A. Install ADOMNET.
B. Install the Analysis Services OLE DB Provider.
C. Run the Data Quality Server Installer.
D. Make the data available for DQS operations.
Answer: C
Explanation:
References: http://msdn.microsoft.com/en-us/library/ff877917.aspx http://msdn.microsoft.com/en-us/library/gg492277.aspx
NEW QUESTION: 2
What happens when you attempt to compile and run the following code?
#include <iostream>
using namespace std;
class BaseClass
{
public:
int *ptr;
BaseClass(int i) { ptr = new int(i); }
~BaseClass() { delete ptr; delete ptr;}
void Print() { cout << *ptr; }
};
void fun(BaseClass x);
int main()
{
BaseClass o(10);
fun(o);
o.Print();
}
void fun(BaseClass x) {
cout << "Hello:";
}
A. It prints: Hello:1
B. It prints: Hello:
C. It prints: 10
D. Runtime error.
Answer: D
NEW QUESTION: 3
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression