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| Section | Objectives |
|---|---|
| Topic 1: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
| Topic 2: Implement Azure AI solutions | - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI |
| Topic 3: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 139
You need to secure an Azure OpenAI resource so that it is only reachable from your virtual network and not from the public internet. What should you configure?
Answer: A
Explanation:
Private Link/private endpoints assign the Azure OpenAI resource a private IP within your VNet and allow disabling public network access, ensuring traffic stays on the Microsoft backbone network and isn't reachable from the public internet.
NEW QUESTION # 140
Hotspot Question
You have an Azure Functions app using the Consumption hosting plan for a company. The app contains the following functions:
You plan to enable dynamic concurrency on the app. The company requires that each function has its concurrency level managed separately.
You need to configure the app for dynamic concurrency.
Which file or function names should you use? To answer, select the appropriate values in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* File name: host.json
* Function name: f3
Azure Functions dynamic concurrency is enabled at the function-app host level in the host.json file . The concurrency configuration section contains the dynamicConcurrencyEnabled property. Setting this value to true enables the Functions host to dynamically learn and adjust concurrency levels for supported triggers rather than requiring fixed manual limits.
Although dynamic concurrency is enabled globally, Microsoft explicitly states that the learned concurrency level is managed independently for each individual function . This allows a resource-intensive function to operate at a lower concurrency level while a lightweight function in the same app can execute with greater concurrency, protecting host health while maximizing throughput.
Of the functions listed, f3 , which uses an Azure Queue Storage trigger , supports dynamic concurrency.
Microsoft currently documents dynamic concurrency support for Azure Queue Storage, Azure Blob Storage, and Azure Service Bus triggers , subject to the required extension versions. HTTP and Timer triggers do not participate in this dynamic-concurrency model.
Therefore, configure dynamic concurrency in host.json , and f3 is the function whose concurrency will be dynamically managed.
Study Guide references: Azure Functions # Concurrency; Dynamic concurrency; host.json; Queue Storage trigger concurrency.
NEW QUESTION # 141
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: B
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 # 142
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Trigger scaling based on queue depth: Azure Service Bus scaler
* Deallocate instances when the message queue is empty: Minimum replicas = 0
* Prevent uncontrolled burst scaling: Maximum replicas = 10
The batch retraining workload must scale according to queue length , so the correct KEDA trigger is the Azure Service Bus scaler . KEDA integrates with Azure Service Bus and monitors queue or subscription metrics, allowing AKS workloads to scale according to the number of pending messages rather than CPU utilization or HTTP request concurrency. Microsoft documents KEDA as the event-driven autoscaling mechanism for AKS and specifically supports Azure-based scalers for queue-driven workloads.
To meet the requirement to deallocate workload instances when no messages remain, configure minimum replicas = 0 . KEDA supports scale-to-zero for event-driven workloads, which is particularly appropriate for intermittent batch or machine-learning processing because no pods need to remain running while the queue is empty. Microsoft specifically identifies scale-to-zero as a KEDA capability and recommends it for sporadic ML workloads where cost optimization is important.
Finally, configure maximum replicas = 10 . A finite maximum replica count establishes an explicit upper scaling boundary, preventing large queue bursts from creating an uncontrolled number of worker replicas and protecting both infrastructure cost and downstream services.
Study Guide references: AKS # KEDA; Azure Service Bus event-driven scaling; minReplicaCount; maxReplicaCount; scale-to-zero; autoscaling limits.
NEW QUESTION # 143
Drag and Drop Question
You are deploying an Azure Function app that retrieves secrets from Key Vault by using a managed identity.
The deployment must ensure that identity and secret configuration are in place before the function code is deployed.
You need to deploy the function app securely.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Create the function app
You must first provision the underlying Azure Functions infrastructure before you can bind an identity or configuration settings to it.
Step 2: Assign a managed identity to the function app
Turning on the managed identity (such as a system-assigned identity) creates a distinct security principal in Microsoft Entra ID for the resource.
Step 3: Grant access to Key Vault
Use the managed identity's principal ID to create an access policy or RBAC role assignment in Key Vault, allowing the app to read secrets.
Step 4: Add Key Vault references to application settings.
Configure the Function App's application settings to point to the Key Vault secret URIs (@Microsoft.KeyVault(...)), which can now be securely resolved by the identity.
Step 5: Deploy the function code
Finally, deploy the application code. This ensures that when the code initializes and executes, all environment variables and secrets are already active and accessible, preventing application startup failures.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 144
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