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| Section | Weight | Objectives |
|---|---|---|
| Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
Die Zertifizierung der Microsoft AI-200 zu erwerben bedeutet mehr Möglichkeiten in der IT-Branche. Wir It-Pruefung haben schon reichliche Erfahrungen von der Entwicklung der Microsoft AI-200 Prüfungssoftware. Unsere Technik-Gruppe verbessert beständig die Prüfungsunterlagen, um die Benutzer der Microsoft AI-200 Prüfungssoftware immer leichter die Prüfung bestehen zu lassen.
140. Frage
Your chat application calls Azure OpenAI Service. You need to prevent the model from returning content that promotes self-harm, even if the prompt is crafted to bypass instructions. What should you configure?
Antwort: B
Begründung:
System messages can be circumvented by adversarial prompting. Content Safety filters operate independently of the model and inspect both input and output, blocking harmful categories (including self-harm) regardless of prompt engineering.
141. Frage
Hotspot Question
You have an Azure Functions app using the Consumption hosting plan for a company. The app contains the following functions:
You plan to enable dynamic concurrency on the app. The company requires that each function has its concurrency level managed separately.
You need to configure the app for dynamic concurrency.
Which file or function names 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: host.json
To configure dynamic concurrency for the app, use the host.json configuration file.
host.json: Global metadata file where you enable the app-level dynamic concurrency setting ("dynamicConcurrencyEnabled": true) under the appropriate extension/extension lock configuration.
Box 2: f3
Apply the host.json file to the Azure Queue trigger function (f3), as dynamic concurrency is natively supported and managed per extension for runtime-driven storage and queue triggers rather than HTTP or timer triggers.
Azure Queue trigger function: Dynamic concurrency in Azure Functions specifically targets runtime-driven background bindings like Azure Queue, Blob, and Service Bus storage triggers, allowing individual management and automatic adjustments of processing limits per function.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-concurrency
142. Frage
You are developing a solution that uses several Azure Service Bus queues. You create an Azure Event Grid subscription for the Azure Service Bus namespace. You use Azure Functions as subscribers to process the messages.
You need to ensure that events can be sent to Azure Event Grid from the queues. The solution must use the principle of least privilege and minimize costs.
Which Azure Service Bus role and tier level should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Verified Answer: Tier: Premium. Role: Azure Service Bus Data Receiver.
Detailed Explanation: Microsoft documents Event Grid integration for Service Bus queues and topics as requiring a Service Bus Premium namespace. The Azure Function subscriber must then receive messages from the queue; the least-privilege built-in data-plane role for that purpose is Azure Service Bus Data Receiver. Data Sender does not permit receiving, and Data Owner is broader than necessary. Premium therefore satisfies the feature prerequisite, while Data Receiver satisfies the stated least-privilege requirement.
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 | Service Bus to Event Grid integration | Authenticate Service Bus with Microsoft Entra ID
143. Frage
You are evaluating a fine-tuned Azure OpenAI model against the base model before promoting it to production. Which Azure AI Foundry capability should you use?
Antwort: A
Begründung:
Azure AI Foundry's evaluation tooling (often via prompt flow or the Evaluation SDK) runs bulk test sets against both models and scores outputs on metrics like groundedness, coherence, and relevance, enabling an objective comparison before promotion.
144. 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 configure a connection string for the partner-facing service according to the technical requirements.
What should you use?
Antwort: B
Begründung:
Technical Requirements
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.
To configure the connection string securely at runtime, you should use App Service environment variables combined with Azure Key Vault references.
Runtime Resolution: When you configure an App Service environment variable using the standard @Microsoft.KeyVault(...) syntax, Azure App Service resolves the secret value dynamically at runtime.
Managed Identity Integration: The App Service automatically uses its assigned system-assigned or user-assigned Managed Identity to authenticate against Azure Key Vault. No credentials are saved locally.
Separation of Concerns: The secret value remains centrally managed in Key Vault, fulfilling the constraint that secrets must not live inside code, configuration files, or image layers.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
145. Frage
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