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NEW QUESTION: 1
Bubba buys a 5% municipal bond maturing in 15 years that is trading at a market price of 85 . What is the nominal yield?
A. 5.1%
B. cannot be determined
C. 5.88%
D. 5.00%
Answer: D
Explanation:
Explanation/Reference:
Explanation: 5.00%. The nominal yield is the coupon rate on the bond.
NEW QUESTION: 2
Welches der folgenden BEST zeigt ein effektives Informationssicherheitsmanagement innerhalb eines Unternehmens?
A. Das Kontrollvermögen wird Parteien zugewiesen, die Verluste aufgrund eines Kontrollfehlers akzeptieren können.
B. Die Governance der Informationssicherheit ist in die organisatorische Governance integriert.
C. Übermäßiges Risiko in einer Abteilung kann von anderen Abteilungen absorbiert werden.
D. Mitarbeiter unterstützen Entscheidungen des Informationssicherheitsmanagements.
Answer: B
NEW QUESTION: 3
You are creating a Windows Communication Foundation service by using Microsoft .NET Framework 3.5. The service is hosted in a managed application.
You need to perform the following tasks:
Publish the service on the following address: net.tcp://localhost:8080/ExamService
Publish the metadata on the following address:
http://localhost:8081/ExamService/metadata?wsdl
Which code fragment should you use?
A. Option A
B. Option D
C. Option B
D. Option C
Answer: A
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