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NEW QUESTION: 1
Which policy will allow a Citrix Administrator to modify all the URLs in the response body from "http://" to "https://" in an SSL offload deployment?
A. add rewrite action Act1 replace_all "HTTP.RES.BODY(200000)" "\"https://\"" -pattern "http://"add rewrite policy Pol1 true Act1 NOREWRITE
B. add rewrite action Act1 replace_all "HTTP.RES.BODY(200000)" "\"https:\"" -pattern "http://"add rewrite policy Pol1 true Act1 NOREWRITE
C. add rewrite action Act1 replace_all "HTTP.RES.BODY(200000)" "\"http:\"" -pattern "https://\"add rewrite policy Pol1 true Act1 NOREWRITE
D. add rewrite action Act1 replace_all "HTTP.RES.BODY(200000)" "\"http://\"" -pattern "https://"add rewrite policy Pol1 true Act1 NOREWRITE
Answer: D
NEW QUESTION: 2
Which two statements are true about a vSAN Cluster when the vCenter Server managing it is offline? (Choose two.)
A. Manage and view vSAN health information using RVC commands.
B. vSAN continues to operate, but vSAN performance metrics will be lost.
C. vSAN continues to operate and all VMs inherit the default vSAN storage policy.
D. Use a Web browser to monitor vSAN through the vSphere Host Client.
E. vSAN health information is available through esxcli commands.
Answer: D,E
NEW QUESTION: 3
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