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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Implement and monitor AI workloads- Monitor performance and troubleshoot issues
- Deploy AI models and services
Implement Azure AI solutions- Implement natural language processing solutions
- Implement knowledge mining with Azure AI Search
- Implement generative AI solutions using Azure OpenAI
- Implement computer vision solutions
Plan and manage Azure AI solutions- Select appropriate Azure AI services
- Monitor and optimize AI solutions
- Plan security and compliance requirements

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Microsoft Developing AI Cloud Solutions on Azure AI-200 Prüfungsfragen mit Lösungen (Q36-Q41):

36. Frage
Drag and Drop Question
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.

Antwort:

Begründung:

Explanation:
Box 1: Feature flag
Use a Feature flag.
Azure App Configuration provides built-in feature management specifically designed to dynamically control application behavior without redeploying code.
Percentage-Based Rollouts: Feature flags support targeting filters (such as the standard Microsoft.Targeting filter), which natively handle percentage-based rollouts and specific audience targeting rules.
Dynamic Changes: You can safely toggle features or change the exposure percentage on the fly directly through the Azure portal or via APIs.
Box 2: Key-value
Key-value is the correct feature to use.
A standard key-value pair is the optimal choice for managing runtime settings like numeric thresholds or token limits.
Direct Data Injection: It allows you to store the specific maximum token limit (e.g., MaxTokensPerRequest = 4096) as an integer or string that your application can parse directly at runtime.
No Extra Overhead: Unlike the other options, a standard key-value pair maps directly to application configuration variables without requiring secondary lookups or conditional logic evaluation.
Box 3: Key Vault reference
Use an Azure Key Vault reference.
Azure App Configuration is optimized for non-sensitive runtime configurations like feature flags, environment names, and general application settings. Sensitive values (such as API keys for OpenAI/LLM models, database passwords, and connection strings) should always be stored natively in Azure Key Vault.
By creating a Key Vault Reference inside Azure App Configuration, you store only the URI pointing to the secret rather than the secret value itself. When your AI application requests configuration data at runtime, the App Configuration provider automatically contacts Key Vault, resolves the reference, and securely injects the actual secret directly into your application's memory pipeline.
Box 4: Key-value
Use a standard configuration key-value pair (or a JSON array within a key).
How to store it: Save the regions as a comma-separated string or a JSON array under a single key.
Key Example: Deployment:SupportedRegionsValue Example: ["eastus", "westeurope",
"southeastasia"]
Content Type: Set the content type to application/json so your application can automatically parse it into a list or array at runtime.
Reference:
https://learn.microsoft.com/en-us/azure/azure-app-configuration/manage-feature-flags
https://turbo360.com/blog/get-azure-key-value-pairs-from-app-configuration
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references


37. Frage
You are provisioning and configuring a Service Bus processor for AI batch jobs.
The processor must connect to an existing queue, register handlers for message and error processing, and then begin receiving messages.
You need to provision and configure the Service Bus processor for message and error handling.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Antwort:

Begründung:

Explanation:

Verified answer: 1) Create the Service Bus client; 2) create the queue processor/receiver; 3) register message and error handlers; 4) start the message processor.
Detailed Explanation: The processor depends on a namespace client/connection, so the client is created first.
Next, the queue-specific processor or receiver is created from that client. Message and error callbacks must be registered before processing starts so incoming messages and failures have defined handlers. Starting the processor is therefore the final step. Dead-lettering failed messages is an optional application settlement decision and is not a prerequisite for constructing and starting the processor.
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 message transfers, locks, and settlement


38. Frage
You maintain multiple versions of a container image in Azure Container Registry.
The production deployment must always run the exact same image build even if tags are changed later.
You need to ensure predictable and immutable image selection during deployment.
What should you do?

Antwort: C

Begründung:
Deploying a container image by its unique SHA-256 digest digest instead of its tag ensures a predictable and immutable deployment. Tags can be overwritten or moved to different image builds, but a digest is a cryptographic hash that never changes.
Reference:
https://container-registry.com/posts/container-image-versioning/


39. Frage
Drag and Drop Question
You are designing Azure Functions for three different backend workloads.
Each workload requires one of the following dispatching models:
- Return an immediate response to a client.
- Process background work from a queue.
- Run code on a fixed schedule.
You need to select the trigger for each requirement.
Which triggers should you select? To answer, move the appropriate triggers to the correct requirements. You may use each trigger 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: HTTP trigger
An HTTP trigger is the best choice for returning an immediate response to a client.
Direct connection: The client makes a standard web request and waits on the same connection.
Low latency: It avoids the delays introduced by routing messages through queues or storage.
Two-way communication: It natively supports returning payload data and HTTP status codes (e.g., 200 OK).
Box 2: Queue Storage Trigger
Azure Queue Storage Trigger or Azure Service Bus Queue Trigger is the best choice to process background work from a queue.
Azure Queue Storage Trigger:
Best for simple, low-cost message queues with high throughput and straightforward FIFO (first-in, first-out) processing needs.
Automatic Scaling: Scales out instances dynamically based on the number of messages waiting in the queue.
Reliability: Automatically handles poison messages and retries failed executions if configured correctly.
Cost Efficiency: Consumes zero compute resources when the queue is empty (especially under the Consumption plan).
Box 3: Timer trigger
An Azure Function with a timer trigger is the standard and ideal choice for running code on a fixed schedule. It acts as a serverless cron job that automatically manages execution intervals, scales automatically, and includes built-in locking mechanisms to prevent duplicate executions across multiple instances.
Reference:
https://levelup.gitconnected.com/request-driven-service-vs-event-driven-service-4bf04d642843?gi=c0bdcb16db5a
https://codilime.com/blog/enabling-first-in-first-out-pattern-microsoft-azure-service-bus-queues-topics/


40. Frage
Drag and Drop Question
A semantic search application queries Azure Database for PostgreSQL and stores document embeddings and metadata in a table with the following columns:
- embedding (pgvector)
- department
- created_at
The application must return the top five most similar documents for a given query embedding only from the finance department.
You need to implement semantic retrieval with metadata filtering.
Which query components should you select? To answer, move the appropriate query components to the correct requirements. You may use each query component 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: WHERE department = 'Finance'
Box 2: ORDER BY embedding <=> :query_vector LIMIT 5
To rank and return the top five most similar documents by similarity in Microsoft Learn, use the ORDER BY embedding <=> [query_vector] expression combined with LIMIT 5, where <=> computes the cosine distance.
Reference:
https://learn.microsoft.com/en-us/azure/postgresql/extensions/how-to-use-pgvector


41. Frage
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