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Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Develop AI solutions using Azure data services30%- Implement vector-enabled databases
  • 1. Azure Cosmos DB for NoSQL with vector search
  • 2. Azure Database for PostgreSQL with pgvector extension
  • 3. Azure Managed Redis for caching, streaming, and vector storage
- Design and optimize data access and retrieval
  • 1. Indexing strategies, query optimization, and consistency models
  • 2. Implement hybrid search and retrieval patterns
Topic 2: Develop containerized AI solutions on Azure25%- 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)
- Monitor and troubleshoot containerized workloads
  • 1. Manage configurations and secrets for containers
  • 2. Log analysis, health checks, and performance monitoring
Topic 3: Secure, monitor, and optimize AI solutions20%- Manage security and configuration
  • 1. Azure Key Vault for secrets, keys, and certificates
  • 2. App Configuration for dynamic settings
  • 3. Managed identities and access control
- Implement observability and reliability
  • 1. OpenTelemetry and Azure Monitor integration
  • 2. Optimize performance, cost, and scalability
  • 3. Logging, metrics, and distributed tracing
Topic 4: Integrate backend services and build event-driven architectures25%- Build serverless APIs and workflows
  • 1. Orchestrate AI pipelines and workflows
  • 2. Azure Functions for AI integration and processing
- 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

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column.
The schema must support vector similarity search and reliable date filtering.
You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,C


NEW QUESTION # 41
You are designing a message based order processing system by using Azure Service Bus.
Each order message must be processed in the same sequence it was received. In addition, a separate audit service must receive copies of all order messages far compliance tracking You need to configure a Service Bus namespace and its entities to meet the ordering and audit requirements Which service bus options should you use? To answer, select the appropriate options m the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 42
A Python API running in ACA must send distributed traces to Azure Monitor.
The API creates spans. However, no traces appear in Azure Monitor.
You need to configure the OpenTelemetry SDK pipeline to export traces to Azure Monitor.
What should you do? To answer, move the appropriate actions to the correct requirements. You may use each action 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.

Answer:

Explanation:

Explanation:
Verified Answer: Register a global trace provider # initialize the application TraceProvider. Export traces to Azure Monitor # create/configure AzureMonitorTraceExporter. Connect exporter to provider # add a span processor. Generate spans # use tracer.start_as_current_span(...).
Detailed Explanation: OpenTelemetry tracing is a pipeline: the application obtains a tracer from a configured provider, creates spans, passes ended spans through a span processor, and exports them using the Azure Monitor exporter. Creating spans alone is insufficient; without the provider/processor/exporter chain, no telemetry reaches Azure Monitor. Log sampling and legacy Application Insights TrackEvent calls are unrelated to the required OpenTelemetry trace-export path.
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 | Enable Azure Monitor OpenTelemetry


NEW QUESTION # 43
A RAG application's retrieval step is returning documents that are topically related but not precisely relevant to the user's question. You need to improve retrieval precision without retraining any model. What should you do?

Answer: A

Explanation:
Azure AI Search's semantic ranker re-scores the initial retrieval results using a secondary language-understanding model, improving precision by pushing more contextually relevant results to the top without any custom model training.


NEW QUESTION # 44
You are deploying a model in Azure AI Foundry and must ensure the endpoint can handle unpredictable bursts of traffic while keeping cost low during idle periods. Which deployment type should you choose?

Answer: A

Explanation:
Standard pay-as-you-go deployments scale with token-based consumption and don't require reserving fixed throughput, making them cost-efficient for variable or bursty workloads. PTU is better suited for predictable, high-volume, latency-sensitive workloads where reserved capacity is justified.


NEW QUESTION # 45
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