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Microsoft AI-200 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Integrate backend services and build event-driven architectures	25%	- Implement messaging and event systems 1. Connect services and expose APIs securely 2. Azure Event Grid for event-driven processing 3. Azure Service Bus for reliable messaging - Build serverless APIs and workflows 1. Azure Functions for AI integration and processing 2. Orchestrate AI pipelines and workflows
Topic 2: Develop containerized AI solutions on Azure	25%	- Monitor and troubleshoot containerized workloads 1. Log analysis, health checks, and performance monitoring 2. Manage configurations and secrets for containers - Implement container hosting environments 1. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS) 2. Azure Container Registry: store, version, manage images 3. Configure scaling, networking, and security for containers
Topic 3: Secure, monitor, and optimize AI solutions	20%	- Implement observability and reliability 1. Logging, metrics, and distributed tracing 2. Optimize performance, cost, and scalability 3. OpenTelemetry and Azure Monitor integration - 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 4: Develop AI solutions using Azure data services	30%	- 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. Implement hybrid search and retrieval patterns 2. Indexing strategies, query optimization, and consistency models

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question 1

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?

A. Azure AI Language custom named entity recognition
B. Azure AI Document Intelligence prebuilt invoice model
C. Azure AI Document Intelligence custom neural model
D. Azure AI Vision Read API

Question 2

Drag and Drop Question
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.

Question 3

An application performs similarity search across 5 million embeddings stored in Azure Database for PostgreSQL with pgvector. Queries often filter by department before ranking by cosine distance.
P95 latency for vector similarity queries exceeds the SLA target. Monitoring shows sustained high CPU use during query execution.
You need to reduce P95 latency for filtered vector similarity queries.
What should you do?

A. Store embeddings as JSON.
B. Increase embedding dimensionality.
C. Create B-tree indexes on frequently filtered metadata columns.
D. Increase statement timeout.

Question 4

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?

A. API key rotation
B. Azure AI Content Safety
C. A private endpoint with Azure Private Link
D. Cross-Origin Resource Sharing (CORS) rules

Question 5

Drag and Drop Question
A Python API retrieves a document from Azure Database for PostgreSQL by using a SQL statement. The API accepts the document ID from user input. The current implementation inserts the document ID directly into the SQL statement.
You need to secure the SQL statement execution by minimizing the possibility of SQL injection.
How should you modify the current implementation? 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.