AZ-204 Exam Dumps - Developing Solutions for Microsoft Azure

Step 1: Build a new application image by using dockerfile

Step 2: Create an alias if the image with the fully qualified path to the registry

Before you can push the image to a private registry, you’ve to ensure a proper image name. This can be achieved using the docker tag command. For demonstration purpose, we’ll use Docker’s hello world image, rename it and push it to ACR.

# pulls hello-world from the public docker hub

$ docker pull hello-world

# tag the image in order to be able to push it to a private registry

$ docker tag hello-word /hello-world

# push the image

$ docker push /hello-world

Step 3: Log in to the registry and push image

In order to push images to the newly created ACR instance, you need to login to ACR form the Docker CLI. Once logged in, you can push any existing docker image to your ACR instance.

Scenario:

Coho Winery plans to move the application to Azure and continue to support label creation.

LabelMaker app

Azure Monitor Container Health must be used to monitor the performance of workloads that are deployed to Kubernetes environments and hosted on Azure Kubernetes Service (AKS).

You must use Azure Container Registry to publish images that support the AKS deployment.

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).

Box 1: QueryType

The SearchParameters.QueryType Property gets or sets a value that specifies the syntax of the search query. The default is 'simple'. Use 'full' if your query uses the Lucene query syntax.

You can write queries against Azure Search based on the rich Lucene Query Parser syntax for specialized query forms: wildcard, fuzzy search, proximity search, regular expressions are a few examples.

Box 2: Facets

The facets property gets or sets the list of facet expressions to apply to the search query. Each facet expression contains a field name, optionally followed by a comma-separated list of name:value pairs.

Box 3: Filter

The Filter property gets or sets the OData $filter expression to apply to the search query.

References:

https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.search.models.searchparameters

https://docs.mi crosoft.com/en-us/azure/search/query-lucene-syntax

https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.search.models.searchparameters.querytype

Methods to Get User Identity and Claims in a .NET Azure Functions App include:

ClaimsPrincipal from the Request Context

The ClaimsPrincipal object is also available as part of the request context and can be extracted from the HttpRequest.HttpContext.

User Claims from the Request Headers.

App Service passes user claims to the app by using special request headers.

To create an internal load balancer, create a service manifest named internal-lb.yaml with the service type LoadBalancer and the azure-load-balancer-internal annotation as shown in the following example:

YAML:

apiVersion: v1

kind: Service

metadata:

name: internal-app

annotations:

service.beta.kubernetes.io/azure-load-balancer-internal: "true"

spec:

type: LoadBalancer

ports:

- port: 80

selector:

app: internal-app

References:

https://docs.microsoft.com/en-us/azure/aks/internal-lb