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| Section | Weight | Objectives |
|---|---|---|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| 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
|
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NEW QUESTION # 100
You need to address the known issue resulting from vector similarity queries.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Detailed Explanation: The objective is to reduce RU consumption from vector similarity queries. Microsoft guidance identifies vector numeric precision and vector-index selection as major cost and performance levers.
Using a lower supported vector precision can reduce storage and processing cost, while quantizedFlat and DiskANN are designed to reduce latency and RU consumption compared with flat search for appropriate data sizes. Strong consistency would increase resource cost rather than solve vector-search efficiency. A regular composite index is not a substitute for the vector index. Option B should be read as reducing vector numeric precision, not changing a generic "indexing precision" setting.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector search in Azure Cosmos DB | Optimize Cosmos DB vector search performance
NEW QUESTION # 101
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: D
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 # 102
You are developing an AI API deployed to ACA. The API requires database credentials that are stored in Key Vault. Key Vault is configured to use Azure RBAC for access control.
The database credentials are rotated periodically by the security team. The application must always use the latest version of each credential without being redeployed and without exposing secrets in code or configurations.
You need to implement a secure secret access strategy that prevents credential exposure and fetches the latest version of each secret at runtime without redeploying the container.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
Step-by-Step Implementation Guide
1. Assign a System-Assigned Managed Identity
2. Configure Key Vault RBAC Role Assignment
Role Selection: Assign the Key Vault Secrets User role to the container app's managed identity.
Scope Limitation: Limit the scope of this assignment to the specific Key Vault or individual secrets rather than the entire resource group.
3. Retrieve the Secret at Runtime Using the SDK
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-use-managed-identity-with-azure-container-apps-to-access-azure-services/view
NEW QUESTION # 103
You need to secure an Azure OpenAI resource so that it is only reachable from your virtual network and not from the public internet. What should you configure?
Answer: C
NEW QUESTION # 104
You deploy a private container image from Azure Container Registry (ACR) to App Service.
App Service must authenticate to ACR to pull the image.
The solution must NOT store static registry credentials.
You need to configure the secure image pull authentication.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
uthenticate App Service to ACR: assign the registry pull role to a managed identity. Avoid static credentials:
configure App Service to use that managed identity for the image pull.
Detailed Explanation: App Service can pull a private image from Azure Container Registry by using a managed identity rather than registry username/password credentials. The identity must be granted an appropriate pull-only registry role at the registry scope, preserving least privilege. A webhook does not authenticate an image pull, and enabling the ACR admin account or storing a registry password would introduce static credentials. The managed-identity approach satisfies both secure authentication and credential- elimination requirements.
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 | Configure a custom container for App Service | Managed identities for Azure resources
NEW QUESTION # 105
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