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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Topic 2: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 3: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
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NEW QUESTION # 72
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 as a GitHub repository secret.
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 # 73
You need to configure a connection string for the partner-facing service according to the technical requirements.
What should you use?
Answer: C
Explanation:
Detailed Explanation: Azure Key Vault references in App Service settings satisfy the requirement to keep secrets out of container images, source control, and directly stored application configuration. App Service resolves the referenced secret at runtime by using the app identity, so the application can consume the value as a normal setting without embedding credentials in the image. GitHub secrets are build/deployment secrets rather than a runtime App Service secret-delivery mechanism. Dockerfile ENV instructions would place secret material in the image configuration and violate the case requirements.
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 | Use Key Vault references for App Service and Functions | Managed identities for Azure resources
NEW QUESTION # 74
You deploy a Linux container image to App Service.
The container requires the following environment variables at runtime:
* A non-sensitive configuration value named MODEL_VERSION
* A database password that must remain secure
You need to configure App Service to provide these environment variables at runtime.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 75
Hotspot Question
You plan to develop an Azure Functions app with an HTTP trigger.
The app must support the following functionality:
- Event-driven scaling
- Ability to use custom Linux images for function execution
You need to identify the app's hosting plan and the maximum amount of time that the app function can take to respond to incoming requests.
Which configuration setting values should you use? To answer, select the appropriate values in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Premium
To fulfill your requirements, you should use the Azure Functions Premium plan (also known as the Elastic Premium plan).
Event-Driven Scaling: It features dynamic, automatic scale-out driven by the Azure scale controller. It can scale down to zero instances when idle, ensuring you only pay for active compute time.
Custom Linux Images: Unlike the base Consumption plan, the Premium plan allows you to deploy and run your functions inside a custom Linux container image. This lets you bring your own custom OS dependencies, specialized tools, or specific runtime environments.
Box 2: 230 seconds
The correct maximum timeout value to use for an HTTP-triggered function is 230 seconds.
Azure Load Balancer Limit: Regardless of the specific Azure Functions hosting plan or timeout configurations you set in host.json, the Azure Functions Scale and Hosting documentation states that an HTTP-triggered function has a hard limit of 230 seconds to respond to a request.
Idle Timeout: This constraint is strictly enforced due to the default idle timeout of the underlying Azure Load Balancer. If your function runs longer than 230 seconds without returning a response, the connection will be dropped, resulting in a timeout error.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-deployment-technologies
https://learn.microsoft.com/en-us/azure/azure-functions/functions-scale
NEW QUESTION # 76
You maintain multiple versions of a container image in Azure Container Registry.
The production deployment must always run the exact same image build even if tags are changed later.
You need to ensure predictable and immutable image selection during deployment.
What should you do?
Answer: D
Explanation:
Use the image ' s SHA-256 manifest digest when defining the production deployment. Azure Container Registry assigns every pushed image manifest a unique digest, and Microsoft explicitly states that pulling an image by digest guarantees the image version being retrieved , even if an identically named tag is later pushed to a different image. A digest reference has the form myregistry.azurecr.io/repository@sha256: < digest > .
Tags such as production or latest are mutable references . By default, a user or pipeline with sufficient permissions can push a different image under the same tag. Microsoft therefore warns against relying on reusable stable tags for production deployments when exact image reproducibility is required.
A scheduled rebuild also creates a new image artifact and therefore cannot guarantee that production executes the original build. In contrast, the manifest digest is content-addressed and resolves to the precise image manifest selected at deployment time.
Thus, for deterministic and immutable production image selection, reference the container image by its SHA digest rather than by a mutable tag .
Study Guide references: Azure Container Registry # image manifests and digests; image addressing; tagging
/versioning recommendations; immutable deployment references.
NEW QUESTION # 77
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