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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions |
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NEW QUESTION # 109
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 # 110
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must retrieve the five most similar documents to an input embedding.
You need to construct the similarity query.
What should you do?
Answer: B
Explanation:
To retrieve the five most similar documents from Azure Cosmos DB for NoSQL using vector search, you must limit the number of results.
When performing vector similarity searches using the VectorDistance() function, you must restrict the output size directly in the query.
Reference:
https://stemhash.com/semantic-search-with-llms/
NEW QUESTION # 111
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: C
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 # 112
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.
You need to implement the SDK components required for the application to access and manage data in Azure Cosmos DB for NoSQL.
Which SDK components should you use? To answer, move the appropriate components to the correct requirements. You may use each 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:
* Initialize the connection by using the account endpoint and key: CosmosClient
* Define shared throughput: Database
* Perform item CRUD operations: Container
The Azure Cosmos DB for NoSQL .NET SDK uses a clear resource hierarchy. CosmosClient is the top-level client object and is responsible for connecting to the Azure Cosmos DB account. Microsoft identifies it as the primary SDK class for accessing account-level resources and databases. It can be initialized by using the account endpoint together with an account key or another supported credential.
A Database represents a database resource within the account. Shared provisioned throughput is configured at the database level , allowing multiple containers within that database to share the same RU/s allocation.
Microsoft documents that setting throughput on the database creates a shared-throughput database.
A Container is the SDK object used to interact with items stored in a Cosmos DB container. Microsoft specifically describes the Container class as the object used for item operations such as creating, reading, updating, deleting, and querying documents.
The Indexing policy option is not used for any of these requirements. It controls how Cosmos DB indexes document properties for query execution, not connectivity, shared throughput, or CRUD access.
Study Guide references: Azure Cosmos DB for NoSQL .NET SDK # CosmosClient; Database; shared throughput; Container; item CRUD operations.
NEW QUESTION # 113
You need to implement trace correlation according to the business requirements.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you se lect.
Answer:
Explanation:
Explanation:
Verified Answer: 1) Instrument the application code with the OpenTelemetry SDK; 2) configure an Azure Monitor trace exporter in the OpenTelemetry SDK; 3) redeploy the instrumented services.
Detailed Explanation: Trace correlation requires the application to create OpenTelemetry spans and an exporter to send those spans to Azure Monitor. Instrumentation must therefore be present before the updated service is deployed. The previous Application Insights SDK instrumentation does not satisfy the case-study requirement that all tracing use OpenTelemetry. Configuring an OpenTelemetry view is unrelated to basic distributed-trace export, and calling TrackEvent is an Application Insights SDK pattern rather than the required OpenTelemetry approach.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 114
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