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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 2: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Topic 3: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Topic 4: Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
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96. Frage
Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
You need to perform a one-time analysis of logs collected from AKS, ACA, and Azure Function apps according to the business requirements.
What should you use?
Antwort: D
Begründung:
Business Requirements
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
A KQL query is the correct tool to use for a one-time, cross-resource analysis to correlate telemetry across AKS, Azure Container Apps (ACA), and Azure Functions.
Cross-Resource Querying: KQL (Kusto Query Language) allows you to use the union operator to combine tables from different workspaces or applications effortlessly.
Correlation Capabilities: You can join logs using standard cloud fields like operation_Id,
_ResourceId, or custom headers to track a single request across your entire architecture.
On-Demand Analysis: It requires no setup time, making it ideal for a one-time investigation directly inside the Azure Monitor Log Analytics interface.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-perform-cross-workspace-queries-in-azure-log-analytics/view
97. Frage
Hotspot Question
You plan to develop an Azure Functions app with an HTTP trigger.
The app must support the following functionality:
- Event-driven scaling
- Ability to use custom Linux images for function execution
You need to identify the app's hosting plan and the maximum amount of time that the app function can take to respond to incoming requests.
Which configuration setting values should you use? To answer, select the appropriate values in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: Premium
To fulfill your requirements, you should use the Azure Functions Premium plan (also known as the Elastic Premium plan).
Event-Driven Scaling: It features dynamic, automatic scale-out driven by the Azure scale controller. It can scale down to zero instances when idle, ensuring you only pay for active compute time.
Custom Linux Images: Unlike the base Consumption plan, the Premium plan allows you to deploy and run your functions inside a custom Linux container image. This lets you bring your own custom OS dependencies, specialized tools, or specific runtime environments.
Box 2: 230 seconds
The correct maximum timeout value to use for an HTTP-triggered function is 230 seconds.
Azure Load Balancer Limit: Regardless of the specific Azure Functions hosting plan or timeout configurations you set in host.json, the Azure Functions Scale and Hosting documentation states that an HTTP-triggered function has a hard limit of 230 seconds to respond to a request.
Idle Timeout: This constraint is strictly enforced due to the default idle timeout of the underlying Azure Load Balancer. If your function runs longer than 230 seconds without returning a response, the connection will be dropped, resulting in a timeout error.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-deployment-technologies
https://learn.microsoft.com/en-us/azure/azure-functions/functions-scale
98. Frage
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
* Correlate failed requests with dependency calls during the last hour.
* Calculate the average dependency duration per operation.
You need to construct the KQL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
Antwort: A,C,E
Begründung:
Use where , join , and summarize .
The where operator filters telemetry to the required scope, including records from the last hour and failed requests. For example, a predicate can restrict timestamp > ago(1h) and request success status in the same filtering expression.
Next, use join to correlate request telemetry with dependency telemetry. Application Insights assigns related telemetry a common operation_Id , and Microsoft specifically demonstrates joining the dependencies and requests tables on operation_Id to identify dependency calls associated with requests.
Finally, use summarize with avg(duration) grouped by the appropriate operation identifier or operation name.
summarize performs aggregation over groups and is therefore the required operator for calculating average dependency duration. Microsoft documents dependency duration as supporting an Avg aggregation.
extend is unnecessary because no derived column is required, while distinct merely removes duplicate rows and cannot calculate averages or correlate two telemetry tables.
A representative structure is:
requests
| where timestamp > ago(1h) and success == false
| join (dependencies | where timestamp > ago(1h)) on operation_Id
| summarize avg_dependency_duration = avg(duration1) by operation_Name
Study Guide references: Azure Monitor Application Insights; KQL where; telemetry correlation using operation_Id; join; summarize; dependency duration analysis.
99. Frage
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You are preparing a production deployment for an Azure Function app. The app will run across multiple environments.
The solution must support environment-specific configuration and prevent secrets from being stored in source control.
You need to develop the solution.
Solution: Store connection strings in the Function app application settings configured in the Azure Portal.
Does the solution meet the goal?
Antwort: B
Begründung:
Azure Functions application settings are the correct mechanism for keeping environment-specific configuration outside the application source code. Microsoft documents that application settings, including connection strings and other configuration values, are defined separately for each function app . This allows development, test, staging, and production deployments to use different values without modifying the application code.
At runtime, Function app settings are exposed to the application as environment variables , so code can retrieve the required connection string or configuration value without embedding it in a configuration file committed to source control. This directly satisfies the requirement to prevent secrets from being stored in the Git repository.
For production-grade secret management, Azure Key Vault references are often preferable because they provide centralized secret lifecycle management and can be combined with managed identities. However, the question asks only whether the proposed solution meets the stated goals. Storing connection strings in Azure Function application settings does satisfy both: values can vary by deployed environment and remain outside source-controlled application files.
Study Guide references: Azure Functions configuration; application settings; environment variables; secure application configuration.
100. Frage
Drag and Drop Question
You deploy an API to Azure Container Apps.
The solution must provide the following functionality:
- Support the concurrent activation of multiple application versions.
- Allocate a specific percentage of incoming requests to a secondary
version.
You need to configure revision behavior.
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.
Antwort:
Begründung:
Explanation:
Box 1: Multiple revisions mode
To test a new revision without affecting production traffic, you must configure the Container App to use Multiple active revisions mode and set the traffic allocation for the new revision to 0%.
Box 2: Traffic splitting
To split traffic between multiple versions, you must switch the container app's revision mode to Multiple revision mode and configure the traffic splitting weights under the Ingress configurations.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/revisions-manage
101. Frage
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Nun ist die Microsoft AI-200 Zertifizierungsprüfung eine beliebte Prüfung in der IT-Branche. Viele IT-Fachleute wollen das Microsoft AI-200 Zertfikat erhalten. So ist die Microsoft AI-200 Zertifizierungsprüfung eine beliebte Prüfung. Das Microsoft AI-200 Zertfikat ist sehr hilfreich, um Ihre Arbeit in der IT-Industrie zu verbessern und Ihr Gehalt zu erhöhen und Ihrem Leben eine zuverlässige Garantie zu geben.
AI-200 Lernhilfe: https://www.zertfragen.com/AI-200_prufung.html