Plan: Standard

Standard support auto-scaling

Instance Count: 10

Max instances for standard is 10.

Scenario:

The REST API’s that support the solution must meet the following requirements:

Allow deployment to a testing location within Azure while not incurring additional costs.

Automatically scale to double capacity during peak shipping times while not causing application downtime.

Minimize costs when selecting an Azure payment model.

Box 1: [BlobTrigger(..)]

Box 2: [Blob(..)]

Azure Blob storage output binding for Azure Functions. The output binding allows you to modify and delete blob storage data in an Azure Function.

The attribute ' s constructor takes the path to the blob and a FileAccess parameter indicating read or write, as shown in the following example:

[FunctionName( " ResizeImage " )]

public static void Run(

[BlobTrigger( " sample-images/{name} " )] Stream image,

[Blob( " sample-images-md/{name} " , FileAccess.Write)] Stream imageSmall)

{

}

Scenario: You must create an Azure Function named CheckUserContent to perform the content checks.

The company’s data science group built ContentAnalysisService which accepts user generated content as a string and returns a probable value for inappropriate content. Any values over a specific threshold must be reviewed by an employee of Contoso, Ltd.

Box 1: volumeMounts

Example:

volumeMounts:

- mountPath: /mnt/secrets

name: secretvolume1

volumes:

- name: secretvolume1

secret:

mysecret1: TXkgZmlyc3Qgc2VjcmV0IEZPTwo=

Box 2: volumes

Box 3: secret

Scenario: Users of the ContentUploadService report that they occasionally see HTTP 502 responses on specific pages.

" 502 bad gateway " and " 503 service unavailable " are common errors in your app hosted in Azure App Service.

Microsoft Azure publicizes each time there is a service interruption or performance degradation.

The az monitor activity-log command manages activity logs.

Note: Troubleshooting can be divided into three distinct tasks, in sequential order:

Observe and monitor application behavior

Collect data

Mitigate the issue

Azure Event Hub is used for telemetry and distributed data streaming.

This service provides a single solution that enables rapid data retrieval for real-time processing as well as repeated replay of stored raw data. It can capture the streaming data into a file for processing and analysis.

It has the following characteristics:

low latency

capable of receiving and processing millions of events per second

at least once delivery

Box 1: Strong

When the consistency level is set to strong, the staleness window is equivalent to zero, and the clients are guaranteed to read the latest committed value of the write operation.

Scenario: Changes to the Order data must reflect immediately across all partitions. All reads to the Order data must fetch the most recent writes.

Note: You can choose from five well-defined models on the consistency spectrum. From strongest to weakest, the models are: Strong, Bounded staleness, Session, Consistent prefix, Eventual

Box 2: SQL

Scenario: You identify the following requirements for data management and manipulation:

Order data is stored as nonrelational JSON and must be queried using Structured Query Language (SQL).

Scenario: You must perform a point-in-time restoration of the retail store location data due to an unexpected and accidental deletion of data.

Before you enable and configure point-in-time restore, enable its prerequisites for the storage account: soft delete, change feed, and blob versioning.

Box 1: AzureServiceTokenProvider()

Box 2: tp.GetAccessTokenAsync( " .. " )

Acquiring an access token is then quite easy. Example code:

private async Task < string > GetAccessTokenAsync()

{

var tokenProvider = new AzureServiceTokenProvider();

return await tokenProvider.GetAccessTokenAsync( " https://storage.azure.com/ " );

}

If you want to read the files in parallel, you cannot use forEach. Each of the async callback function calls does return a promise. You can await the array of promises that you ' ll get with Promise.all.

Scenario: Capacity issue: During busy periods, employees report long delays between the time they upload the receipt and when it appears in the web application.