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NEW QUESTION: 1
Which two methods can be used to remove the previous vendor profiles the mobile device?
A. Go to My Devices portal in ISE and click corporate wipe
B. Use the "full wipe" option and reset the device to factory setting
C. Vendor profiles cannot be remove
D. Use the "corporate wipe" option offered by the vendor
E. Disable the ISE profiling feature
Answer: A,D

NEW QUESTION: 2
A bag contains 12 red, 3 blue, 6 green, and 4 yellow marbles. If a marble is drawn from the bag at random, what is the probability that the marble will be either blue or yellow?
A. 16%
B. 28%
C. 25%
D. 12%
E. 7%
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Add the number of marbles to get the total number in the bag; 12 + 3 + 6 + 4 = 25. Therefore, 25 is the number of possible outcomes. Seven marbles are either blue or yellow. Seven is the number of favorable outcomes;

NEW QUESTION: 3
Tran Holten, a quantitative analyst with Smith, Kleen & Beetchnutty Brokerage, has just been informed of an important error in one of his recent statistical endeavors. Specifically, in one hypothesis test, Mr. Holten rejected a null hypothesis that later was proven to be true. Which of the following best describes this type of error in hypothesis testing? Further, if the confidence level of the test were increased, would the probability of this error increase, decrease, or remain unchanged?
A. Type I error; decrease
B. Type II error; remain unchanged
C. Type I error; remain unchanged
D. Type II error; decrease
E. Type I error; increase
F. Type II error; increase
Answer: A
Explanation:
Explanation/Reference:
Explanation:
In this example, Tran Holten has incorrectly rejected a null hypothesis. This type of error in hypothesis testing is called a Type I error. In hypothesis testing, the Type I error is given much more attention than the Type II error. In most hypothesis tests, the probability of a null hypothesis is equal to the significance level of the test. A significance level of 0.01, for example, indicates that a 1% chance exists that the null hypothesis will be rejected when it is indeed true. Another way to think of the probability of a Type I error is to observe the following relationship:
{Probability of a Type I error = (1 - confidence level)}.
For example, a confidence level of 95% leaves a 5% probability of a Type I error occurring. If this confidence level were to increase to say, 98%, then the probability of a Type I error would reduce to 2%.
As you can see, there is a relationship between the confidence level of a test and the probability of a Type I error. If the confidence level of the test in this example were to increase, then the probability of a Type I error would decrease.