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| Section | Objectives |
|---|---|
| Topic 1: Implement Azure AI solutions | - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions |
| Topic 2: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| 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 # 98
You are developing a microservices-based application that uses Azure Container Apps. The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
Report generation must be implemented as follows:
Process reports every Sunday at midnight.
Minimize app resources during report generation.
Record metrics in the same location as other containers.
Use the same virtual network as other containers.
Answer:
Explanation:
Explanation:
NEW QUESTION # 99
You are reviewing an Azure Function app that processes incoming order requests for a company. The function must:
* Accept order submissions from an external client application.
* Require controlled access for security
* Return a response containing the processed request payload.
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:
Verified answer: Yes; No; Yes; No.
Detailed Explanation: `auth_level=FUNCTION` means invocation requires a function key unless a stronger platform authentication layer is configured, so the first statement is true. The route explicitly permits POST, not GET. The function reads `req.get_body()` and returns that value in the HttpResponse, so the response contains the request body. No schema, type, required-field, or other payload validation is performed before the response is constructed, making the final statement false.
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 Functions HTTP trigger
NEW QUESTION # 100
Your application stores conversation history so a multi-turn chatbot can reference earlier turns, but the context window is limited. You need to keep the most relevant history within token limits.
What should you implement?
Answer: A
NEW QUESTION # 101
You are developing a .NET application that uses Azure Cosmos DB for NoSQL to store application data The application uses the Azure Cosmos DB for NoSQL SDK to interact with the database account. The application must perform the following tasks
* Initialize the connection by using the account endpoint and key.
* Define shared throughput.
* Perform create, read, update, and delete (CRUD) operations on items stored in a container
Answer:
Explanation:
Explanation:
Verified Answer: Initialize connection: CosmosClient. Define shared throughput: Database. Perform item CRUD: Container.
Detailed Explanation: The Cosmos DB SDK follows the service resource hierarchy. CosmosClient represents the account connection and is initialized with the account endpoint and credential. Shared throughput is configured at the database level when multiple containers share database throughput. Item create, read, update, and delete operations are performed through a container object because containers hold items and define partitioning/indexing behavior. An indexing policy is configuration attached to a container, not the SDK object used for CRUD.
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 | Cosmos DB databases, containers, and items
NEW QUESTION # 102
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: A
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 # 103
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