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NEW QUESTION: 1
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: Accuracy, Precision, Recall, F1 score and AUC.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION: 2

A. Option C
B. Option D
C. Option B
D. Option A
Answer: C

NEW QUESTION: 3
An auditor prepared a workpaper that consisted of a list of employee names and identification numbers as well as the following statement:
"A statistical sample of 40 employee personnel files was selected to verify that they contain all documents required by company policy 501 (copy attached). No exceptions were noted." The auditor did not place any audit verification symbols on this workpaper. Which of the following changes would most improve the auditor's workpaper?
A. Justification for the sample size.
B. Use of audit verification symbols to show that each file was examined.
C. Listing of the actual documents examined for each employee.
D. Removal of the employee names to protect their confidentiality.
Answer: A