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NEW QUESTION: 1
You have a Microsoft Office SharePoint Server 2007 Service Pack 2 (SP2) server farm.
You plan to upgrade to SharePoint Server 2010.
You need to Identify all potential upgrade issues before you install SharePoint Server 2010.
What should you do?
A. Download and run the Microsoft Best Practices Analyzer for Windows SharePoint Services 3.0 and the 2007 Microsoft Office System.
B. Run stsadm.exe -o preupgradecheck.
C. Install Windows PowerShell 2.0 and run the test-spcontentdatabase cmdlet.
D. Run psconfig.exe -cmd upgrade from the SharePoint Server 2010 installation files.
Answer: B
Explanation:
Explanation/Reference:
BURGOS MNEMO: "potential upgrade issues" = "preupgradecheck"
Run the pre-upgrade checker (SharePoint Server 2010) http://technet.microsoft.com/en-us/library/cc262231.aspx
NEW QUESTION: 2
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 model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:
ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
NEW QUESTION: 3
The Universal Containers company plans to upload target data from an external tool to Einstein Analytics so they can calculate the Sales team target attainments.
The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales representative, the target amount, and the month of the target. For each sales representative, the file contains a target for every month of the current year as well as all previous years.
Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?
A. Upsert
B. Overwrite
C. Append
D. Update
Answer: B