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| Section | Objectives |
|---|---|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 95
Drag and Drop Question
You are provisioning and configuring a Service Bus processor for AI batch jobs.
The processor must connect to an existing queue, register handlers for message and error processing, and then begin receiving messages.
You need to provision and configure the Service Bus processor for message and error handling.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Create the Service Bus client
This establishes the initial connection to your Azure Service Bus namespace using a connection string or Azure Identity credentials.
Step 2: Create the Service Bus processor for the queue
Once you have a client, you call its factory method to instantiate a processor dedicated specifically to targeting your queue Step 3: Register message and error handlers The processor requires event handlers assigned to its processing and error properties to explicitly dictate what happens when a message arrives or an exception occurs Step 4: Start the message processor The processor remains idle until you call the method to start receiving, which initiates the internal loop that listens for and pulls down messages.
Reference:
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-dotnet-get-started-with-queues?tabs=passwordless
NEW QUESTION # 96
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key in Azure Key Vault and reference it from an App Service application setting.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 97
You need to deploy Azure function resources and apps by using an automated, version-controlled CI/CD pipeline that supports declarative infrastructure deployment. What should you use?
Answer: B
Explanation:
Detailed Explanation: GitHub Actions is the correct orchestration mechanism among the options because the case requires an automated, version-controlled pipeline and Bicep-based declarative deployment. Microsoft documents GitHub Actions as a supported way to deploy Bicep and automate Azure resource deployment.
Local Git, Azure Functions Core Tools, and Azure CLI can participate in development or scripted deployment, but they do not by themselves satisfy the stated requirement for a repository-driven, repeatable CI
/CD pipeline.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Deploy Bicep with GitHub Actions | Automate Function app resource deployment
NEW QUESTION # 98
You are developing an AI API deployed to ACA. The API requires database credentials that are stored in Key Vault. Key Vault is configured to use Azure RBAC for access control.
The database credentials are rotated periodically by the security team. The application must always use the latest version of each credential without being redeployed and without exposing secrets in code or configurations.
You need to implement a secure secret access strategy that prevents credential exposure and fetches the latest version of each secret at runtime without redeploying the container.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C,E
Explanation:
Step-by-Step Implementation Guide
1. Assign a System-Assigned Managed Identity
2. Configure Key Vault RBAC Role Assignment
Role Selection: Assign the Key Vault Secrets User role to the container app's managed identity.
Scope Limitation: Limit the scope of this assignment to the specific Key Vault or individual secrets rather than the entire resource group.
3. Retrieve the Secret at Runtime Using the SDK
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-use-managed-identity-with-azure-container-apps-to-access-azure-services/view
NEW QUESTION # 99
You need to configure image build automation based on the technical requirements.
Which settings should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Trigger for container images build: Base image update
* Implementation of image builds: ACR Task
The correct trigger is Base image update because Proseware requires every custom container image to be rebuilt automatically whenever its Docker Hub base image changes. Azure Container Registry Tasks can detect base-image dependencies from the Dockerfile FROM instruction and automatically trigger a rebuild when that base image is updated. Microsoft confirms that ACR Tasks can track base images in public repositories such as Docker Hub and Microsoft Container Registry, as well as images stored in Azure Container Registry.
The build implementation must be an ACR Task . This directly satisfies the technical requirement that image- build automation run inside Azure Container Registry , eliminating dependencies on local developer machines or separate external build services. ACR Tasks provides cloud-based container building and can automate builds based on source-code commits, base-image updates, or scheduled triggers.
A GitHub workflow could orchestrate CI/CD, but using it as the actual image-building implementation would conflict with the explicit requirement that builds execute inside ACR. Docker Compose defines multi- container applications; it is not the native ACR build automation mechanism. A Commit trigger addresses source changes rather than the stated base-image update requirement, while Scheduled builds would introduce unnecessary periodic execution.
Study Guide references: Azure Container Registry Tasks; base-image dependency tracking; automated rebuilds; Docker Hub base-image triggers; cloud-native container builds.
NEW QUESTION # 100
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