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NEW QUESTION: 1
Collaboration can be defined as working together to jointly produce a deliverable or make a decision, whereas coordination is:
A. Pair programming.
B. Agreeing on the design.
C. Agreeing on a due date.
D. Sharing information.
Answer: D
NEW QUESTION: 2



A. From Windows PowerShell on Server1, run the Set-VmSwitch cmdlet.
B. From Windows PowerShell on Server1, run the Set-VmNetworkAdapterFailoverConfiguration cmdlet
C. From Windows PowerShell on VM1, run the Set-VmNetworkAdapterTeamMapping cmdlet.
D. From Windows PowerShell on Server1, run the cmdlet.
Answer: C
NEW QUESTION: 3
A customer has requested that the OIM self-service UI be customized to show their new logo. After running the customization, you are not able to see the changes. The old logo still remains visible on the main page.
What is the reason for this?
A. ThecommandImageLinkcomponent was deleted.
B. ThecommandImageLinkcomponent icon property is set asNULL.
C. As part of the customization process, during sandbox creation, the "Activate Sandbox" option was not selected.
D. ThecommandImageLinkcomponent binding property was set toFalse.
Answer: C
NEW QUESTION: 4
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:
Explanation:
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative
