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Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
You are configuring an Azure-hosted AI application that uses Azure App Configuration to manage its runtime settings.
The application must provide the following functionality:
- Support percentage-based, targeted rollouts.
- Maximize token limits per request.
- Store a key that is retrieved securely at runtime.
- Reference a list of supported deployment regions.
You need to configure the App Configuration features for the app.
Which features should you configure? To answer, move the appropriate App Configuration features to the correct configuration items. You may use each App Configuration features 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.
Explanation:
* Support percentage-based, targeted rollouts: Feature flag
* Maximize token limits per request: Key-value
* Store a key that is retrieved securely at runtime: Key Vault reference
* Reference a list of supported deployment regions: Key-value
Azure App Configuration provides three distinct mechanisms for these scenarios. A Feature flag is the correct choice for percentage-based and targeted rollouts. Feature Management supports conditional activation based on filters such as percentage allocation, users, groups, and other targeting criteria, making it appropriate for gradual deployment and controlled experimentation.
A Key-value is the correct mechanism for ordinary runtime configuration data such as a model token limit or a list of supported deployment regions. These values are application settings rather than secrets or feature- state controls. The application can retrieve and refresh them dynamically without redeployment. A token-limit setting such as MaxTokensPerRequest and a region list such as SupportedRegions fit naturally into App Configuration key-value entries.
For sensitive data, use a Key Vault reference . App Configuration stores only the reference to the Azure Key Vault secret rather than the secret value itself. At runtime, the application resolves the secret securely, typically by using a managed identity. This avoids embedding credentials or sensitive keys in configuration values.
Therefore, the correct mapping is Feature flag # Key-value # Key Vault reference # Key-value .
Study Guide references: Azure App Configuration # Feature Management; key-values; Key Vault references; dynamic application configuration.
You deploy a new revision of an app in Azure Container Apps.
You need to gradually shift production traffic to the revision while monitoring performance. In addition, you need to be able to quickly roll back. Which two actions should you perform?
Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
- A. Restart the revision.
- B. Increase replica count.
- C. Use traffic splitting
- D. Enable multiple revision mode.
- E. Use single revision mode
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A company has an Azure Cosmos DB for NoSQL account. The account is configured for session consistency.
Data is written to a single Azure region and data can be read from three Azure regions.
An application that will access the Azure Cosmos DB for NoSQL container data using an SDK has the following requirements:
* Roads from the application must return the most recent committed version of an Item from any Azure region.
The container items should not automatically be deleted.
You need to implement the changes to the Azure Cosmos DB for NoSQL account. What should you do? To answer, select the appropriate options in the answer area.
Explanation:
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
Explanation:
Verified Answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | AKS scaling overview | Kubernetes services in AKS
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. Create a pgvector index on the embedding column.
- C. Increase statement timeout.
- D. Remove the similarity ORDER BY clause.
