Microsoft AI-200 Exam Dumps

For an Azure Function app exposing a public API with the given requirements, use an HTTP trigger:

• Validates incoming request data - HTTP triggers receive and can validate request data, returning responses immediately
• Returns results immediately to caller - HTTP triggers support synchronous request-response patterns
• Microsoft Entra ID authentication - HTTP triggers can be secured with Entra ID authentication via authorization policies
• Auto-scaling - Azure Functions automatically scale based on HTTP request volume

Other triggers (Timer, Blob, Queue, Service Bus) are event-driven for asynchronous processing and don't support direct HTTP request-response patterns required for a public API.

The correct answer is the first option. Azure Container Apps has built-in KEDA (Kubernetes Event-driven Autoscaling) support, and KEDA includes a native Azure Service Bus scaler that can trigger rapid scaling based on queue depth. You define these scalers in your revision configuration, enabling sub-second response to queue changes. This is the native, supported approach for event-driven scaling in Container Apps.

The second option (Monitor autoscale rules) is less responsive—metric-based autoscaling has longer latency than event-driven scaling. The third option is incorrect because App Service has limited autoscaling capabilities for custom queue-based triggers and isn't designed for fine-grained event-driven scaling like KEDA. The fourth option is overly complex and introduces maintenance overhead when KEDA handles this natively.

The correct answer identifies the proper Service Bus pattern: configuring a DLQ with a max delivery count threshold ensures failed messages (after retries) are automatically moved to the DLQ, where a separate function can process them for logging, alerting, and reprocessing. Option B conflates Event Grid with Service Bus failure handling; Event Grid is for event distribution, not dead-letter processing. Option C avoids the core requirement of capturing and investigating failures. Option D unnecessarily decouples the message queue from the storage layer and adds complexity without leveraging Service Bus's built-in failure handling mechanisms.

To implement secure access to Azure Key Vault for the API:

1. Use Managed Identity for authentication - Managed Identity (system-assigned or user-assigned) eliminates the need to store credentials in configuration files. The identity is managed by Azure and credentials are issued automatically
2. Create identities with minimal permissions - Use role-based access control (RBAC) to grant only the specific permission needed: 'Get' access to secrets. This minimizes blast radius if the identity is compromised

This combination ensures the API authenticates without stored credentials, operates with least-privilege access, and limits exposure in case of compromise. Avoid using connection strings or shared keys stored in configuration.

Note: The question references a 'known issue resulting from vector similarity queries' but does not provide the options. Vector similarity query issues typically require:

• Creating appropriate vector indexes (pgvector extension in PostgreSQL)
• Configuring proper distance metrics and index types
• Tuning query parameters for performance
• Potentially enabling specific extensions or features

Without the available options, specific actions cannot be determined. Please provide the answer choices to select the correct solutions.