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NEW QUESTION: 1
DRAG DROP


Answer:
Explanation:

NEW QUESTION: 2
Welche der folgenden Möglichkeiten verringert die Wahrscheinlichkeit potenzieller Folgen von Projektrisikoereignissen?
A. Korrekturmaßnahme
B. Vorbeugende Maßnahme
C. Fehlerbehebung
D. Risikomanagement
Answer: B

NEW QUESTION: 3
Case Study 3 - Proseware, Inc
Background
You are a developer for Proseware, Inc. You are developing an application that applies a set of governance policies for Proseware's internal services, external services, and applications. The application will also provide a shared library for common functionality.
Requirements
Policy service
You develop and deploy a stateful ASP.NET Core 2.1 web application named Policy service to an Azure App Service Web App. The application reacts to events from Azure Event Grid and performs policy actions based on those events.
The application must include the Event Grid Event ID field in all Application Insights telemetry.
Policy service must use Application Insights to automatically scale with the number of policy actions that it is performing.
Policies
Log Policy
All Azure App Service Web Apps must write logs to Azure Blob storage. All log files should be saved to a container named logdrop. Logs must remain in the container for 15 days.
Authentication events
Authentication events are used to monitor users signing in and signing out. All authentication events must be processed by Policy service. Sign outs must be processed as quickly as possible.
PolicyLib
You have a shared library named PolicyLib that contains functionality common to all ASP.NET Core web services and applications. The PolicyLib library must:
* Exclude non-user actions from Application Insights telemetry.
* Provide methods that allow a web service to scale itself
* Ensure that scaling actions do not disrupt application usage
Other
Anomaly detection service
You have an anomaly detection service that analyzes log information for anomalies. It is implemented as an Azure Machine Learning model. The model is deployed as a web service.
If an anomaly is detected, an Azure Function that emails administrators is called by using an HTTP WebHook.
Health monitoring
All web applications and services have health monitoring at the /health service endpoint.
Policy loss
When you deploy Policy service, policies may not be applied if they were in the process of being applied during the deployment.
Performance issue
When under heavy load, the anomaly detection service undergoes slowdowns and rejects connections.
Notification latency
Users report that anomaly detection emails can sometimes arrive several minutes after an anomaly is detected.
Relevant portions of the app files are shown below. Line numbers are included for reference only and include a two-character prefix that denotes the specific file to which they belong.


Relevant portions of the app files are shown below. Line numbers are included for reference only and include a two-character prefix that denotes the specific file to which they belong.

You need to resolve a notification latency issue.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Set Always On to true.
B. Ensure that the Azure Function is using an App Service plan.
C. Set Always On to false.
D. Ensure that the Azure Function is set to use a consumption plan.
Answer: A,B
Explanation:
Azure Functions can run on either a Consumption Plan or a dedicated App Service Plan. If you run in a dedicated mode, you need to turn on the Always On setting for your Function App to run properly. The Function runtime will go idle after a few minutes of inactivity, so only HTTP triggers will actually "wake up" your functions. This is similar to how WebJobs must have Always On enabled.
Scenario: Notification latency: Users report that anomaly detection emails can sometimes arrive several minutes after an anomaly is detected.
Anomaly detection service: You have an anomaly detection service that analyzes log information for anomalies. It is implemented as an Azure Machine Learning model. The model is deployed as a web service.
If an anomaly is detected, an Azure Function that emails administrators is called by using an HTTP WebHook.
References:
https://github.com/Azure/Azure-Functions/wiki/Enable-Always-On-when-running-on-dedicated- App-Service-Plan