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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Topic 2: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 3: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
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NEW QUESTION # 62
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 address the known issue resulting from vector similarity queries.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Known Issues: RU consumption spikes during vector similarity queries.
To address the RU (Request Unit) consumption spikes during vector similarity queries in Azure Cosmos DB, you should perform the following two steps:
Modify the indexing precision of the vector fields
Change the vector index type from flat to quantizedFlat or diskANN
Indexing Precision Tuning: Modifying parameters like vector quantization (e.g., configuring quantizedByteSize) alters the size and precision of the stored vector elements. Lowering the precision decreases memory usage and index size, allowing faster searches that consume far fewer RUs at the expense of marginal recall accuracy.
Index Type Modification: A standard flat index conducts a brute-force k-nearest neighbors (kNN) exact search across every document. This requires vast computational overhead and causes RU spikes as your dataset grows. Transitioning to quantizedFlat or diskANN leverages compression techniques and advanced graph-traversal algorithms to perform approximate nearest neighbor (ANN) searches, dropping query latency and compute costs significantly.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/vector-search-performance-tips
NEW QUESTION # 63
An AI application retrieves configuration values from App Configuration. The application uses static configuration.
You need to implement a solution that supports dynamic configuration updates and minimizes latency for application requests.
What should you do?
Answer: B
Explanation:
You should cache configuration values with a refresh interval.
Enables Dynamic Configuration: Azure App Configuration providers natively support a cache- polling mechanism. Once the refresh interval expires, the application checks for updates and reloads configuration values automatically without requiring an application restart.
Minimizes Latency: By pulling configuration values from a local, in-memory cache for all intermediary requests, you completely bypass the network latency of making a remote API call to Azure on every single application request.
Prevents Throttling: Relying on cached values prevents your application from exhausting your App Configuration resource's request quota.
Reference:
https://learn.microsoft.com/en-us/azure/azure-app-configuration/howto-best-practices
NEW QUESTION # 64
You are developing several microservices to run on Azure Container Apps. External HTTP ingress traffic has been enabled for the Microservices.
A deployed microservice must be updated to allow users to test new features. You have the following requirements:
* Enable and maintain a single URL for the updated microservice to provide to test users.
* Update the microservice that corresponds to the current microservice version.
You need to configure Azure Container Apps.
Which features should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: Single URL for test users: Revision label. Current microservice activation behavior:
Revision mode.
Detailed Explanation: Azure Container Apps revision labels provide a stable label-specific URL that can be reassigned to a different revision, which is appropriate for a consistent test endpoint. Revision mode controls whether one or multiple revisions can be active at the same time and therefore governs how the current and updated application versions are activated. The container image and registry identify what is deployed, but they do not provide the stable test URL or the application-level revision activation behavior requested.
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 Container Apps revisions | Azure Container Apps revision labels
NEW QUESTION # 65
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.
Answer:
Explanation:
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
NEW QUESTION # 66
You are implementing an application by using Azure Event Grid to push near-real-time information to customers.
You have the following requirements:
* You must send events to thousands of customers that include hundreds of various event types.
* The events must be filtered by event type before processing.
* Authentication and authorization must be handled by using Microsoft Entra ID.
* The events must be published to a single endpoint
You need to implement Azure Event Grid
Solution: Publish events to a partner topic. Create an event subscription for each customer.
Does the solution meet the goal?
Answer: B
Explanation:
Detailed Explanation: The proposed partner-topic design does not match the stated need for one publisher endpoint that can fan events into thousands of logically distinct customer topics. Event Grid domains are specifically designed to expose a single publishing endpoint while containing large numbers of topics, with subscriptions and filtering applied at the topic level. Partner topics are intended for partner-published SaaS events and do not substitute for the domain architecture required by this high-scale custom publishing scenario.
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 | Azure Event Grid event domains | Authenticate Event Grid publishing with Microsoft Entra ID
NEW QUESTION # 67
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