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NEW QUESTION: 1
You have a web app named MainApp. You are developing a triggered App Service background task by using the WebJobs SDK.
This task automatically invokes a function in the code whenever any new data is received in a queue.
You need to configure the services.
Which service should you use for each scenario? To answer, drag the appropriate services to the correct scenarios. Each service 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:
https://docs.microsoft.com/en-us/azure/azure-functions/functions-compare-logic-apps-ms-flow-webjobs Logic Apps --> Azure DevOps: source control, testing, support, automation, and manageability in Azure Resource Manager
NEW QUESTION: 2
内部監査契約内のリスクは、次のように定義されます。
A. 組織のポリシー、計画、および手順を順守していない、または関連する法律と規制を遵守していない。
B. 目標の達成に影響を与える可能性のある、発生したイベントの不確実性。
C. 運用またはプログラムの確立された目的と目標を達成できない。
D. 取引の残高またはクラスおよび関連するアサーションに、財務諸表にとって重要である可能性のある虚偽の陳述が含まれている確率。
Answer: B
NEW QUESTION: 3
Which of the following statements is true:
I. When averaging quantiles of two Pareto distributions, the quantiles of the averaged models are equal to the geometric average of the quantiles of the original models based upon the number of data items in each original model.
II. When modeling severity distributions, we can only use distributions which have fewer parameters than the number of datapoints we are modeling from.
III. If an internal loss data based model covers the same risks as a scenario based model, they can can be combined using the weighted average of their parameters.
IV If an internal loss model and a scenario based model address different risks, the models can be combined by taking their sums.
A. II and III
B. III and IV
C. I and II
D. All statements are true
Answer: D
Explanation:
Explanation
Statement I is true, the quantiles of the averaged models are equal to the geometric average of the quantiles of the original models.
Statement II is correct, the number of data points from which model parameters are estimated must be greater than the number of parameters. So if a distribution, say Poisson, has one parameter, we need at least two data points to estimate the parameter. Other complex distributions may have multiple parameters for shape, scale and other things, and the minimum number of observations required will be greater than the number of parameters.
Statement III is true, if the ILD data and scenarios cover the same risk, they are essentially different perspectives on the same risk, and therefore should be combined as weighted averages.
But if they cover completely different risks, the models will need to be added together, not averaged - which is why Statement IV is true.
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