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?

• A. Local Git deployment
• B. GitHub Actions
• C. Azure CLI
• D. Azure Functions Core Tools

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You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.
The application must use managed identity for authentication.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation:

Verified Answer: Yes; Yes; No.
Detailed Explanation: The get_secret call supplies an explicit version value, so it retrieves that particular version of dbPassword. DefaultAzureCredential can authenticate to Key Vault by using the Azure-hosted application's managed identity without embedding a client secret in the application. Because the code requests a fixed version, later secret rotation creates a newer version but subsequent executions of this exact code continue to request the specified old version. To follow the latest version automatically, the version parameter must be omitted.
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 | Managed identities for Azure resources | Use Key Vault references for App Service and Functions

You are implementing a Retrieval-Augmented Generation (RAG) system by using the native vector search capabilities of Azure Cosmos DB for NoSQL API.
You have a container named Documents that stores technical articles. Each article includes a property named embedding.
You must ensure that the system can perform efficient similarity searches between user queries and the stored articles.
You need to configure the database resources to support semantic retrieval.
Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Explanation:
* Facilitate mathematical distance calculations between data points: Configure a vector index.
* Define the document schema for high-dimensional data: Store data as a numeric array.
Azure Cosmos DB for NoSQL supports native vector search by storing embeddings directly in JSON documents and defining a vector embedding policy plus a vector index on the embedding path. Microsoft documents that vector search compares a query vector with stored vectors by calculating similarity or distance through the VectorDistance() system function. A vector index materially improves this process by reducing search latency, increasing throughput, and lowering RU consumption compared with an unindexed vector scan.
The embedding property itself must be stored as an array of numeric values . Microsoft examples show embedding properties such as " contentVector " : [2, -1, 4, ...] and define the associated vector policy with attributes including path, data type, dimensions, and distance function. This representation is required because embeddings are high-dimensional numerical vectors generated by an embedding model.
A composite index optimizes queries involving multiple scalar properties but does not provide vector- distance indexing. A Base64-encoded string cannot be used directly for native vector similarity calculations because Cosmos DB expects the vector field to contain numeric values matching the configured dimensionality.
Study Guide references: Azure Cosmos DB for NoSQL # vector embedding policies; vector indexes; VectorDistance(); numeric embedding arrays; native vector search.

You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:

You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
Reference the Azure Key Vault without requiring any changes to the Java code.
Dynamically add and remove instances of the Azure Functions host based on the number of incoming application events.
Ensure that instances are perpetually warm to avoid any cold starts.
Connect to a VNet.
Authentication to the Azure Key Vault instance must be removed if the Azure Function application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
Which three 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.

Explanation:

Step 1: Create the Azure Functions app with a Consumption plan type.
Use the Consumption plan for serverless.
Step 2: Create a system-assigned managed identity for the application.
Create a system-assigned managed identity for your application.
Key Vault references currently only support system-assigned managed identities. User-assigned identities cannot be used.
Step 3: Create an access policy in Key Vault for the application identity.
Create an access policy in Key Vault for the application identity you created earlier. Enable the " Get " secret permission on this policy. Do not configure the " authorized application " or applicationId settings, as this is not compatible with a managed identity.
Reference:
https://docs.microsoft.com/en-us/azure/app-service/app-service-key-vault-references