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NEW QUESTION: 1
Which statement regarding Engagement Designer (ED) workflows in Avaya Oceana solution is true, if you want to avoid launching both old and new flows during processing?
A. ED allows you to deploy both old and new ED workflows In the ED without any problems.
B. ED does not allow you to deploy a new call flow when there is an existing call flow for the same channel.
C. The existing ED workflows should be deleted before deploying new ED workflows for the same channel.
D. The existing ED workflows should be renamed before deploying new ED workflows for the same channel.
Answer: D

NEW QUESTION: 2
A public sector service organization is considering whether to use a balanced scorecard or a value for money approach based on the three Es to assess its performance.
Which of the following are correct comparisons of the balanced scorecard and value for money based on the three Es as performance measurement frameworks?
Select ALL that apply.
A. Efficiency is measured as one of the three Es but the balanced scorecard does not measure efficiency.
B. If the organization wishes to consider both financial and non-financial performance then the balanced scorecard should be used rather than the three Es.
C. The three Es approach was designed for public sector service organizations, but the balanced scorecard approach can also be used in the public sector.
D. The public's satisfaction with the organization's services can be measured by both the three Es and the balanced scorecard.
E. The balanced scorecard is concerned with meeting the organization's objectives whereas the three Es approach is concerned only with reducing costs.
Answer: C,D

NEW QUESTION: 3
You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line.
Which metric should you review?
A. Recall
B. Coefficient of determination
C. Mean absolute error
D. Precision
E. Root Mean Square Error
Answer: B
Explanation:
Explanation
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.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model


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