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| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Topic 2: Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement computer vision solutions |
| Topic 3: Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
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NEW QUESTION # 48
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 # 49
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?
Answer: C
Explanation:
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
NEW QUESTION # 50
A large retail company operates online and physical stores. The company tracks inventory levels in real time to manage stock efficiently across all locations. You develop an Azure Event Grid solution to handle events generated by the inventory management system deployed to Azure.
You need to implement a subscription filter that dynamically adjusts to seasonal changes in product demand.
Which event filter should you use?
Answer: C
Explanation:
To handle dynamic, seasonal changes in product demand within Azure Event Grid, you should use Advanced Filters with a string or numeric comparison that evaluates event data attributes, typically paired with an automated external process (like an Azure Function or Logic App) to dynamically update the subscription filter rules via the Event Grid management API as seasons change Reference:
https://techcommunity.microsoft.com/blog/analyticsonazure/a-technical-implementation-guide-for-multi-store-retail-environments/4488418
NEW QUESTION # 51
Drag and Drop Question
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a downstream processor.
Which three 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.
Answer:
Explanation:
Explanation:
Step 1: Register a provider
Because this is a newly provisioned Azure subscription, you must register the Microsoft.EventGrid resource provider before you can deploy any Event Grid components.
Step 2: Create a custom topic
Since you are routing AI inference events originating from your application, you need to create a custom topic to act as the endpoint where these events are sent.
Step 3: Create an event subscription
You must create an event subscription to define the routing rules. This is where you configure filtering to ensure only high-confidence events are directed to your downstream processor.
Reference:
https://www.c-sharpcorner.com/article/building-event-driven-ai-workflows-with-azure-event-grid-and-net/
NEW QUESTION # 52
You plan to deploy a web app to App Service on Linux. You create an App Service plan. You create and push a custom Docker image that contains the web app to Azure Container Registry.
You need to access the console logs generated from inside the container in real-time.
How should you complete the Azure CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
nable container logging with `az webapp log config --docker-container-logging filesystem`, then stream it with `az webapp log tail`.
Detailed Explanation: The Azure CLI exposes separate operations for enabling App Service container logging and viewing the live stream. For a custom Linux container, docker/container logging must first be written to the App Service filesystem. The `az webapp log tail` command then starts real-time log tracing.
Commands such as `show` or `download` inspect configuration or retrieve historical files; they do not provide the requested live stream from the running container.
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 | Azure CLI: az webapp log
NEW QUESTION # 53
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