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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 21
Your Azure AI Search index must support both exact keyword matches on product SKUs and semantic similarity search on product descriptions in a single query. What should you configure?
Answer: C
Explanation:
Hybrid search in Azure AI Search combines traditional keyword (BM25) search over text fields with vector similarity search over embedding fields in a single query, giving both exact-match precision (SKUs) and semantic recall (descriptions).
NEW QUESTION # 22
You are designing an Azure Function app that processes large image uploads submitted by users through an HTTP endpoint.
The solution must:
* Prevent client timeouts by decoupling image processing from the initial upload request.
* Support automatic retry behavior for failed processing attempts.
* Scale the background processing independently of the rate of incoming HTTP uploads.
You need to design a scalable and reliable asynchronous processing solution.
Which two actions should you implement? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
The correct architecture uses an Azure Storage queue as an asynchronous buffer between the HTTP ingestion path and the computationally expensive image-processing workload. The HTTP-triggered function should accept the upload, enqueue a work message, and return promptly rather than waiting for image processing to finish. Microsoft specifically recommends asynchronous patterns for long-running HTTP operations because an HTTP-triggered Azure Function can encounter the Azure Load Balancer response timeout even though the underlying function continues executing.
A queue-triggered function then performs image processing independently. Azure Functions applies target- based scaling to Azure Queue Storage triggers based on queued workload, allowing worker capacity to expand as the backlog increases rather than being directly tied to HTTP arrival processing.
For reliability, Azure Queue-triggered Functions provide retry behavior. The visibilityTimeout setting controls the delay before a failed message becomes available for another attempt, while maxDequeueCount controls how many processing attempts occur before the message is transferred to the < queue-name > - poison queue. The default maximum is five attempts.
C defeats the asynchronous requirement. D can be useful for tracking upload state, but it does not itself provide workload decoupling, retry handling, or queue-driven scaling.
Study Guide references: Azure Functions # Queue Storage triggers and bindings; event-driven scaling; retry
/poison queue behavior; asynchronous HTTP processing.
NEW QUESTION # 23
You deploy a production Azure Function app that connects to an Azure SQL Database.
The solution must provide the following functionality:
* Prevent secrets from being exposed in source control.
* Support secret rotation without redeploying the function app.
* Avoid downtime during credential updates.
You need to configure secure and maintainable secret management. What should you configure?
Answer: A
Explanation:
Detailed Explanation: Key Vault references in Function/App Service application settings keep the connection secret outside source control while allowing the platform to resolve it at runtime by identity. When a referenced secret version is not fixed, rotation can be picked up without rebuilding or redeploying the function code. `local.settings.json` is intended for local development and must not be used as a production secret store. A source-controlled parameter file or hard-coded connection string directly violates the security requirement.
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
NEW QUESTION # 24
You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
[C]
You can configure an Azure Service Bus scale rule in Azure Container Apps, which utilizes built- in KEDA (Kubernetes Event-driven Autoscaling) to monitor queue length and scale the app from zero instances to meet your processing demands.
[B]
Configure the scaling rule to allow for the termination of all active replicas is the correct action to take.
To ensure that no compute resources are consumed when the queue is empty, you must set the minimum replica count (minReplicas) to 0 in your Azure Container Apps scaling configuration.
When the Azure Service Bus queue has zero messages, KEDA will scale the container replicas down to zero, stopping all compute billing.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/scale-app
NEW QUESTION # 25
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: D
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 # 26
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