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NEW QUESTION: 1
Dell EMCPowerStoreアレイはどのDellEMCストレージシステムからデータをネイティブにインポートできますか？
A. PowerVault
B. VNX2
C. VNX
D. VMAX
Answer: B

NEW QUESTION: 2
You have an Azure subscription that contains an Azure Service Bus named Bus1.
Your company plans to deploy two Azure web apps named App1 and App2. The web apps will create messages that have the following requirements:
* Each message created by App1 must be consumed by only a single consumer
* Each message created by App2 will be consumed by multiple consumers.
Which resource should you create for each web app? To answer, drag the appropriate resources to the correct web apps. Each resource may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

NEW QUESTION: 3
Which of the following distribution assumptions will produce the lowest probability of exceeding an extreme value, assuming identical means and variances?
A. a normal mixture distribution
B. t-distribution
C. a normal distribution
D. a distribution with kurtosis = 5
Answer: C
Explanation:
Explanation
An 'extreme value' will be a value that will lie in the tails. We need to determine the distribution that will have the least weight in the tails so that the probability of exceeding this tail value is minimum across the given choices.
The t-distribution, a distribution with kurtosis > 3 and a normal mixture distribution are all distributions with tails fatter than that for a normal distribution. A normal distribution will have the 'thinnest' tails among the choices and therefore the lowest probability of exceeding a given tail event value.
A note about the t-distribution: Leptokurtic distributions (those that have kurtosis>3, ie kurtosis greater than that for a normal distribution) generally appear to have higher peaks on their PDF graphs. The t-distribution is flatter, and actually appears lower than a normal distribution, which may make one think that it has a lower kurtosis and therefore should have thinner tails than a normal distribution. But that is not so, and the "visual" inspection test fails for inferring the kurtosis from just looking a the shape of the distribution. The kurtosis of a t-distribution is given by the formula {3 + 6/(d - 4)}, where d is the degrees of freedom and d > 4. Therefore the kurtosis of a t-distribution is always greater than 3 as "6/(d-4)" will always be a positive number being added to 3. Therefore there is no conflict between a t-distribution having fatter tails than a normal distribution as it has a higher kurtosis, even though it appears 'lower' on a graph when superimposed with a normal distribution.

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