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NEW QUESTION: 1

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

NEW QUESTION: 2
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)
A. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
B. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
C. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
Answer: C,E

NEW QUESTION: 3

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

NEW QUESTION: 4
Your network contains a Web server named Web1 that runs Windows Server 2008 R2. Web1 is located on the perimeter network.
Web contains a Web site named Public.
You need to prevent the Public Web site from responding to requests that originate from the internal
network.
Which feature should you configure?
A. Authorization Rules
B. IP Address and Domain Restrictions
C. Authentication
D. IIS Manager Permissions
Answer: B