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| Section | Objectives |
|---|---|
| Topic 1: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 56
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?
Answer: A
Explanation:
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
NEW QUESTION # 57
Drag and Drop Question
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome 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: Supports automatic rotation.
When an AI application retrieves a secret from Azure Key Vault without specifying a version, it always fetches the latest active version. This choice impacts your security and operations in the following ways:
Latest Version Fetch: The vault automatically serves the current active secret value.
No Code Changes: Your application reads the new credential immediately after rotation without a code redeployment.
Zero Downtime: Automated rotation pipelines can update the secret in Key Vault while the app seamlessly switches to the new token.
Box 2: Requires manual update after rotation
Using a specific version identifier when retrieving a secret from Key Vault means the application always requests one exact version of that secret. This choice requires manual update after rotation because the application code or configuration pins a specific version ID and will not fetch the new value when the secret rotates.
Box 3: Prevents credential storage
Prevents credential storage is the correct outcome of using managed identity for authentication.
No credentials to manage: Managed identities eliminate the need for developers to manage credentials entirely.
Automatic token handling: Azure automatically creates an identity for the application in Microsoft Entra ID and handles the service principal's lifecycle behind the scenes, ensuring that no secrets or connection strings are ever hardcoded or stored in configuration files.
Box 4: Introduces credential exposure
Introduces credential exposure and Requires manual update after rotation are the primary outcomes of storing a client secret in environment variables.
Storing clear-text secrets in environment variables poses a significant security risk. Any process, compromised dependency, or user with access to the execution environment (such as container details, application logs, or process dumps) can easily read them.
Reference:
https://medium.com/simform-engineering/building-automated-secret-rotation-using-azure-key-vault-and-net-01e0701f0a2e
https://www.varonis.com/blog/azure-managed-identities
NEW QUESTION # 58
An ACA app processes messages from an Azure Storage queue.
The app must scale automatically based on messages in a specific Azure Storage queue by using a Kubernetes Event-driven Autoscaler (KEDA) custom scale rule. You need to configure the required scale rule values.
Which two values should you configure? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Detailed Explanation: A KEDA custom scale rule must identify what scaler to use and which resource within that scaler should be observed. For Azure Storage queues, the trigger type selects the Storage Queue scaler and the queue name identifies the specific queue whose message count drives scaling. Polling interval and maximum replicas are general scaling controls, but they do not define the required queue-specific trigger.
Therefore trigger type and queue name are the essential rule values tested here.
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 | Set scaling rules in Azure Container Apps
NEW QUESTION # 59
Drag and Drop Question
You are investigating high latency in an AI search application that processes millions of requests daily. Telemetry is stored in Azure Monitor Logs.
You must create a KQL query that correlates information from the AppRequests table and the AppDependencies table. The query must meet the following requirements:
- Include only data from the last 24 hours.
- Filter for failed requests only.
- Calculate the average duration of dependencies, grouped by operation.
The query must be optimized for performance by minimizing the initial data scan.
You need to create the query.
Which five actions should you perform in sequence? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Select the requests table
Starts the query stream with the primary base table.
Step 2: Apply a time filter
Such as (where timestamp >= ago(1d))
Restricts the dataset to the last 24 hours immediately to optimize performance by reducing the scan size.
Step 3: Filter the failed requests
where success == false or resultCode checks
Narrows down rows to only failures before performing resource-heavy operations.
Step 4: Join the dependencies table
(join AppDependencies): Correlates the filtered request subset with dependency data using common tracking identifiers.
Step 5: Summarize average dependency duration by operation (summarize avg(duration) by operation) Computes the final aggregated metrics grouped by the operation name.
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/logs/get-started-queries
NEW QUESTION # 60
An AI application uses a database. The database credential rotates every 30 days.
The application currently requires a manual update each time the credential rotates.
You need to ensure that the application always uses the latest secret version without manual updates.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Configure a Key Vault rotation policy to automate periodic credential generation and updates.
Retrieve secrets without a version identifier (versionless reference) so the app automatically resolves the newest active version.
Incorrect:
[Not B]
To stop manual updates, you should not retrieve secrets by specifying a version identifier, because locking in a version stops the application from seeing newer updates.
Reference:
https://learn.microsoft.com/en-us/azure/key-vault/secrets/secure-secrets
NEW QUESTION # 61
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