Microsoft AI-200 : Developing AI Cloud Solutions on Azure

AI-200 pass collection

Exam Code: AI-200

Exam Name: Developing AI Cloud Solutions on Azure

Updated: Sep 15, 2026

Q & A: 144 Questions and Answers

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Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Develop containerized AI solutions on Azure25%- 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. Azure Container Registry: store, version, manage images
  • 2. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS)
  • 3. Configure scaling, networking, and security for containers
Topic 2: 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 3: 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
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

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question #1

You deploy multiple instances of a change feed processor to handle a high ingestion rate within Azure Cosmos DB for NoSQL.
Each processor instance must process a different subset of partitions.
You need to ensure the workload is load-balanced across all processor instances.
What should you configure?

  • A. lease container
  • B. autoscale throughput
  • C. strong consistency
  • D. indexing precision
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).

Question #2

A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?

  • A. Scale the deployment to more replicas.
  • B. Drain and reboot the node hosting the pod.
  • C. Decrease the initialDelaySeconds for the container liveness probe.
  • D. Inspect the pod events and container logs.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

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.

Reveal Solution  Discussion  0

Correct Answer:


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.

Question #4

You provisioned an Azure Cosmos DB for NoSQL account named account1 with the default consistency level.
You plan to configure the consistency level on a per request basis. You plan to request Consistent Prefix consistency on a per-request basis.
You need to identify the resulting consistency level for read and write operations.
Which levels result from this configuration? To answer, select the appropriate options in the answer a rea.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Verified Answer: Read operations: Consistent Prefix. Write operations: Session (the account default remains effective for writes).
Detailed Explanation: Azure Cosmos DB allows a client or request to override consistency for reads.
Microsoft explicitly notes that such an override applies only to reads; it does not change how writes are committed and replicated under the account's configured consistency. Because a new account uses Session consistency by default, requesting Consistent Prefix affects the reads while the account continues to use its Session consistency behavior for writes. This distinction is the key point tested by the hotspot.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Manage Cosmos DB consistency levels | Cosmos DB consistency level choices

Question #5

You have an Event Grid subscription that triggers an Azure Function.
You need to prevent loss of events in case the endpoint returns an HTTP 400 response. Which action should you perform?

  • A. Configure a dead letter destination.
  • B. Configure a retry policy.
  • C. Implement optimistic batching.
  • D. Implement asynchronous handshake validation.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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