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| Section | Weight | Objectives |
|---|---|---|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
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NEW QUESTION # 120
You are developing an application that must extract structured field data (invoice number, total, vendor name) from scanned invoices in multiple layouts. You need a solution that requires no custom model training. What should you use?
Answer: B
Explanation:
The prebuilt invoice model in Azure AI Document Intelligence is trained to recognize common invoice fields (vendor, total, line items, invoice number) out of the box across varied layouts, requiring no training data or custom model.
NEW QUESTION # 121
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Verified answer: 1) Enable ingress; 2) add the required DNS records; 3) validate ownership of the custom domain; 4) add the custom domain to the Container App; 5) bind the certificate.
Detailed Explanation: A custom HTTPS hostname requires an ingress endpoint, DNS proof that the requester controls the hostname, the hostname association itself, and a TLS certificate bound to that hostname.
DNS records must exist before ownership validation can succeed. Once ownership is validated, the domain can be associated with the Container App and the certificate can be bound to provide HTTPS. Skipping ingress or DNS validation would prevent the hostname from being correctly routed and secured.
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 | Custom domains and certificates in Container Apps
Topic 1, Proseware Inc. Case Study
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.
NEW QUESTION # 122
You have an Azure Service Bus namespace that contains a topic named Topic1.
You plan to create a subscription named Sub1 to Topic1. In Sub1, you plan to filter messages from Topic1 based on their system properties and apply an action that will annotate each filtered message.
You need to configure the filtering.
How should you configure the filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Filter type: Use the SQL type.
Filtering action: Copy a message and annotate its metadata.
Use a SQL filter because Azure Service Bus SQL subscription filters can evaluate both system properties and application-defined properties . Microsoft documents that system properties are referenced using the sys. scope in SQL expressions, while custom properties can use the user. scope. This makes SQL filtering appropriate when the subscription must evaluate message system metadata.
More importantly, Service Bus supports a SQL rule action together with a SQL filter. After a message satisfies the filter, the rule action can add, replace, or remove message properties by using a SQL-like action expression. Microsoft explains that the action executes after the message matches the filter and before it is selected into the subscription .
A topic subscription does not modify the original message published to Topic1. Service Bus selects and copies the matching message into the subscription ' s virtual queue , and any annotation performed by the rule action is private to that subscription copy. Therefore, the accurate action description is copy a message and annotate its metadata , rather than modifying the original message.
The Boolean filter only represents always-true/false selection behavior, while Correlation filters provide efficient property matching but do not provide the SQL action behavior required here.
Study Guide references: Azure Service Bus # topic subscriptions; SQL filters; system properties; SQL rule actions; subscription message copies.
NEW QUESTION # 123
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.
Answer:
Explanation:
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
NEW QUESTION # 124
You are building a multi-agent solution in Azure AI Foundry where one agent handles scheduling and another handles billing questions, and a request may need both. What should you implement?
Answer: B
Explanation:
Multi-agent orchestration patterns use a coordinating (orchestrator) agent to decompose a request, delegate sub-tasks to specialized agents, and aggregate their outputs -- this is more maintainable and accurate than one broad prompt or model trying to cover disparate domains.
NEW QUESTION # 125
......
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