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| Section | Weight | Objectives |
|---|---|---|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| 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 # 50
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: B,C
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
Topic 1 : Proseware Inc. Case Study 7
Topic 2 : Fabrikam Inc 8
Topic 3 : Standalone Questions Set 127
TOTAL 142
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 # 51
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 system topic. Create an event subscription for each customer.
Does the solution meet the goal?
Answer: B
Explanation:
Detailed Explanation: System topics represent events emitted by Azure services and are not the correct resource for an application publishing its own hundreds of event types to thousands of customers through one custom endpoint. Event Grid domains are intended for exactly this type of application-level multi-tenant topic organization and expose one publishing endpoint for many topics. Therefore a system topic with a subscription per customer does not satisfy the stated publishing model, even though Event Grid supports filtering and secure delivery in other contexts.
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 Verification completed using official Microsoft Learn sources only. Original exhibits have been retained unchanged.
NEW QUESTION # 52
Hotspot Question
You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.
The application must use managed identity for authentication.
You review the following code segment that retrieves a secret from Key Vault.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
No, this code will not retrieve the latest version of dbPassword.
Instead, it explicitly retrieves the specific version designated by the string "123".
To pull the latest active version of a secret, omit the version parameter entirely from the get_secret function.python# Change this line:
secret = client.get_secret("dbPassword", version="123")
# To this line:
secret = client.get_secret("dbPassword")
Box 2: Yes
Yes, the code will successfully authenticate to Azure Key Vault without storing client secrets when deployed to an Azure-hosted environment with a managed identity enabled.
DefaultAzureCredential Behavior: This class is a sequential chaining credential mechanism.
When deployed to Azure (such as Azure App Service, Azure VMs, or Azure Functions), it automatically looks for an active Managed Identity environment endpoint to fetch an access token.
Zero Secret Storage: Because the platform relies on the underlying Azure host infrastructure to handle identity token generation, you do not need to hardcode client secrets, passwords, or certificates anywhere inside your application code or configuration settings.
Box 3: No
No, subsequent calls to this code will not retrieve the new version.
By explicitly including version="123" in the get_secret method, you are requesting a specific immutable version of the secret.
Reference:
https://learn.microsoft.com/en-us/azure/key-vault/secrets/quick-create-python
NEW QUESTION # 53
An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic refresh.
The solution must support dynamic configuration refresh while ensuring that secrets are NOT stored in App Configuration.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D,E
Explanation:
[A]
Setting up a cache expiration or polling interval enables the application to detect configuration changes periodically without restarting, fulfilling the requirement for dynamic refresh.
[B]
Using Azure Managed Identities eliminates hardcoded credentials. It allows your application to securely authenticate to both Azure App Configuration and Azure Key Vault.
[C]
Moving API keys to Azure Key Vault adheres to security best practices. Azure App Configuration can reference these secrets securely using Key Vault references, ensuring secrets are never stored as plaintext in the configuration store.
Reference:
https://smartbridge.com/azure-ai-foundry-enterprise-guide-2/
NEW QUESTION # 54
Your solution must answer questions about numeric data in large Excel-based reports, for example:
" What was Q3 revenue for the West region? "
Which approach is most appropriate?
Answer: D
Explanation:
For precise questions over tabular numeric data, convert the workbook data into a structured relational/table representation and expose controlled query functionality to the AI application through function calling. This preserves columns such as quarter, region, revenue, product, and fiscal period as typed fields that can be filtered, aggregated, and compared deterministically. Microsoft demonstrates natural-language-to-SQL patterns in which a user ' s question is translated into a query against structured PostgreSQL data, while Foundry function calling allows a model or agent to invoke application-defined query tools with structured parameters.
This is materially more reliable than embedding spreadsheet rows as unstructured text. Vector search is optimized for semantic similarity , not exact arithmetic, grouping, filtering, or aggregation. A query such as " Q3 revenue for the West region " is fundamentally a structured lookup or aggregation problem. SQL can express those constraints explicitly and return the authoritative numeric result.
Microsoft Foundry also supports agents working with structured data and provides examples where agent tools analyze numeric files and return computed values.
Relying on parametric model knowledge cannot provide current organization-specific financial figures. Azure AI Vision could OCR an image of a spreadsheet, but converting an already structured workbook into an image would destroy useful schema and increase extraction error risk.
Study Guide references: Microsoft Foundry agents; function calling; structured data access; natural- language-to-SQL; grounding AI responses in enterprise data.
NEW QUESTION # 55
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