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Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Integrate backend services and build event-driven architectures25%- Build serverless APIs and workflows
  • 1. Azure Functions for AI integration and processing
  • 2. Orchestrate AI pipelines and workflows
- Implement messaging and event systems
  • 1. Azure Service Bus for reliable messaging
  • 2. Connect services and expose APIs securely
  • 3. Azure Event Grid for event-driven processing
Topic 2: Secure, monitor, and optimize AI solutions20%- Implement observability and reliability
  • 1. OpenTelemetry and Azure Monitor integration
  • 2. Optimize performance, cost, and scalability
  • 3. Logging, metrics, and distributed tracing
- Manage security and configuration
  • 1. App Configuration for dynamic settings
  • 2. Managed identities and access control
  • 3. Azure Key Vault for secrets, keys, and certificates
Topic 3: Develop AI solutions using Azure data services30%- Design and optimize data access and retrieval
  • 1. Indexing strategies, query optimization, and consistency models
  • 2. Implement hybrid search and retrieval patterns
- Implement vector-enabled databases
  • 1. Azure Database for PostgreSQL with pgvector extension
  • 2. Azure Cosmos DB for NoSQL with vector search
  • 3. Azure Managed Redis for caching, streaming, and vector storage
Topic 4: Develop containerized AI solutions on Azure25%- Monitor and troubleshoot containerized workloads
  • 1. Manage configurations and secrets for containers
  • 2. Log analysis, health checks, and performance monitoring
- Implement container hosting environments
  • 1. Azure Container Registry: store, version, manage images
  • 2. Configure scaling, networking, and security for containers
  • 3. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS)

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q118-Q123):

NEW QUESTION # 118
You process Azure Service Bus messages that require a dependent external API call.
If the API is temporarily unavailable, you must delay processing of the message without incrementing the delivery count. You need to find a way to process the message when the API is available while keeping the message accessible. Which message action should you perform?

Answer: D

Explanation:
Detailed Explanation: Deferral is intended for a message that cannot be processed now because a particular dependency is temporarily unavailable, while the application wants to keep the message in the main queue for later retrieval by sequence number. Complete would permanently remove it, dead-letter would move it aside as a failed message, and abandon immediately releases the lock for redelivery. Deferral therefore best matches the requirement to postpone the individual message while keeping it available for deliberate later processing.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Service Bus message deferral | Service Bus message transfers, locks, and settlement


NEW QUESTION # 119
You are developing an application that must extract structured field data (invoice number, total, vendor name) from scanned invoices in multiple layouts. You need a solution that requires no custom model training. What should you use?

Answer: A

Explanation:
The prebuilt invoice model in Azure AI Document Intelligence is trained to recognize common invoice fields (vendor, total, line items, invoice number) out of the box across varied layouts, requiring no training data or custom model.


NEW QUESTION # 120
You are reviewing the Python tracing configuration for an application that must send distributed traces to Azure Monitor.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
* The code configures the tracer provider before any spans are created: Yes
* The configuration exports traces synchronously: No
* The configuration enables export to Azure Monitor: Yes
The first statement is Yes. The code calls trace.set_tracer_provider(TracerProvider()) before obtaining the tracer and before any span-creation operation occurs. In OpenTelemetry, retrieving a tracer by using trace.
get_tracer() does not itself create a span; spans are created later through methods such as start_span() or start_as_current_span().
The second statement is No. The code uses BatchSpanProcessor, which batches completed spans and exports them through a background processing mechanism rather than exporting every span synchronously on the application ' s execution path. OpenTelemetry documentation states that BatchSpanProcessor batches ended spans before sending them to the configured exporter, and the Python implementation uses a background thread for export processing.
The third statement is Yes. AzureMonitorTraceExporter is instantiated with an Azure Monitor/Application Insights connection string and passed to BatchSpanProcessor. That processor is then registered with the tracer provider. Microsoft provides this same pattern specifically for exporting OpenTelemetry traces to Azure Monitor/Application Insights.
Study Guide references: Azure Monitor OpenTelemetry; TracerProvider; BatchSpanProcessor; AzureMonitorTraceExporter; distributed tracing.


NEW QUESTION # 121
You need to give an Azure OpenAI-based agent the ability to call a company's internal REST API to check order status during a conversation. What should you implement?

Answer: D

Explanation:
Function calling lets you describe available functions/tools with a JSON schema; the model decides when to invoke them and with what arguments, and your application executes the actual API call and returns results to the model.


NEW QUESTION # 122
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 # 123
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