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NEW QUESTION: 1
複数のAWSアカウントを持つ企業が使用量を組み合わせてボリュームディスカウントを取得できるサービスはどれですか。
A. AWS組織
B. AWS Trusted Advisor
C. AWSサーバー移行サービス
D. AWS予算
Answer: A

NEW QUESTION: 2
情報システム監査人は、最近の組織再編により、進行中のインフラストラクチャプロジェクトから期待されるメリットが変化したと指摘しています。情報システム監査人の最善の推奨事項は次のうちどれですか?
A. ビジネス影響分析(BIA)を確認および更新します
B. ビジネスの目標と目的を確認する
C. ビジネスケースを確認して再承認する
D. 新しい実現可能性調査を実施する
Answer: A

NEW QUESTION: 3
For a given mean, which distribution would you prefer for frequency modeling where operational risk events are considered dependent, or in other words are seen as clustering together (as opposed to being independent)?
A. Binomial
B. Poisson
C. Gamma
D. Negative binomial
Answer: D
Explanation:
Explanation
An interesting property that distinguishes the three most used distributions for modeling event frequency is that for a given mean, their variances differ. The ratio of variance to mean (the variance-mean ratio, calculated as variance/mean) can then be used to decide the kind of distribution to use. Both the variance and the mean can be estimated from available data points from the internal or external loss databases, or the scenario exercise.
The variance-mean ratio reflects how dispersed a distribution is. (In the PRMIA handbook, the variance to mean ratio has been described as the "Q-Factor".) The Poisson distribution has its mean equal to its variance, and therefore the variance to mean ratio is 1. For the negative binomial distribution, this ratio is always greater than 1, which means there is greater dispersion compared to the mean - or more intervals with low counts as well as more intervals with high counts. For the binomial distribution, the variance to mean ratio is less than one, which means it is less dispersed than the Poisson distribution with values closer to the mean.
In a situation where operational risk events are seen as clustering together, or dependent, the variance will be higher and it would be more appropriate to use the negative binomial distribution.