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Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Develop containerized AI solutions on Azure25%- Implement container hosting environments
  • 1. Configure scaling, networking, and security for containers
  • 2. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS)
  • 3. Azure Container Registry: store, version, manage images
- Monitor and troubleshoot containerized workloads
  • 1. Log analysis, health checks, and performance monitoring
  • 2. Manage configurations and secrets for containers
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 Event Grid for event-driven processing
  • 2. Azure Service Bus for reliable messaging
  • 3. Connect services and expose APIs securely
Secure, monitor, and optimize AI solutions20%- Manage security and configuration
  • 1. Managed identities and access control
  • 2. Azure Key Vault for secrets, keys, and certificates
  • 3. App Configuration for dynamic settings
- Implement observability and reliability
  • 1. OpenTelemetry and Azure Monitor integration
  • 2. Logging, metrics, and distributed tracing
  • 3. Optimize performance, cost, and scalability
Develop AI solutions using Azure data services30%- 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
- Design and optimize data access and retrieval
  • 1. Implement hybrid search and retrieval patterns
  • 2. Indexing strategies, query optimization, and consistency models

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q57-Q62):

NEW QUESTION # 57
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?

Answer: B

Explanation:
Creating a B-tree index on the department column will help, but only if you use the correct indexing strategy, as a standard B-tree index alone is often ignored during vector searches.
When you run a query that filters by metadata before performing a vector similarity search, PostgreSQL must choose between filtering the rows first or searching the vector index first. In many cases, a standard B-tree index on the metadata column combined with an HNSW/IVFFlat index on the vector column results in pre-filtering that defaults to a sequential scan, or post- filtering that returns fewer results than requested.
Reference:
https://learn.microsoft.com/en-us/azure/horizondb/ai/vector-search-pgvector


NEW QUESTION # 58
You are developing several microservices to deploy to a new Azure Kubernetes Service cluster The microservices manage data stored m Azure Cosmos DB and Azure Blob storage. The data is secures by using customer managed keys stored in Azure Key Vault.
You must automate key rotation for all Azure Key Vault keys and allow for manual key rotation. Keys must rotate every three months. Notifications of expiring keys must be sent before key expiry.
You need to configure key rotation and enable key expiry notifications.
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 # 59
A python web API uses OpenTelemetry for tracing. The call downstream service function makes an outbound HTTP request by using the requests library. The following code is the only OpenTelemetry configuration in the application:

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

Answer:

Explanation:

Explanation:


NEW QUESTION # 60
You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification.
What should you implement?

Answer: A

Explanation:
A retrieval-augmented generation (RAG) architecture is the correct mechanism when generated answers must be traceable to specific source documents. RAG first retrieves relevant document chunks from a search index or other knowledge source, supplies those chunks to the language model as grounding context, and then generates an answer based on that retrieved information. Microsoft specifically documents that RAG enables responses containing citations back to source content .
For reliable citation generation, each retrieved chunk should carry identifying metadata such as a source identifier, document title, section, page, or URL . Microsoft's Azure RAG guidance recommends labeling retrieved chunks and including source metadata with them, then explicitly instructing the model to cite the corresponding sources in its response. This allows users to verify individual claims against the underlying documents.
Modern Azure AI Search agentic retrieval can also return structured references and grounding metadata , specifically designed for citation linking.
Increasing top_p only alters token sampling. Content filtering addresses safety, not provenance. Using a base model without retrieval provides no authoritative document linkage.
Study Guide references: RAG architecture; grounding data; retrieval metadata; Azure AI Search references; prompt engineering for citations.


NEW QUESTION # 61
You are implementing an application by using Azure Event Grid to push near-real-time information to customers.
You have the following requirements:
* You must send events to thousands of customers that include hundreds of various event types.
* The events must be filtered by event type before processing.
* Authentication and authorization must be handled by using Microsoft Entra ID.
* The events must be published to a single endpoint
You need to implement Azure Event Grid
Solution: Publish events to a partner topic. Create an event subscription for each customer.
Does the solution meet the goal?

Answer: B

Explanation:
Detailed Explanation: The proposed partner-topic design does not match the stated need for one publisher endpoint that can fan events into thousands of logically distinct customer topics. Event Grid domains are specifically designed to expose a single publishing endpoint while containing large numbers of topics, with subscriptions and filtering applied at the topic level. Partner topics are intended for partner-published SaaS events and do not substitute for the domain architecture required by this high-scale custom publishing scenario.
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 | Azure Event Grid event domains | Authenticate Event Grid publishing with Microsoft Entra ID


NEW QUESTION # 62
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