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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
| Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 115
You are building a multi-agent solution in Azure AI Foundry where one agent handles scheduling and another handles billing questions, and a request may need both. What should you implement?
Answer: B
Explanation:
Multi-agent orchestration patterns use a coordinating (orchestrator) agent to decompose a request, delegate sub-tasks to specialized agents, and aggregate their outputs -- this is more maintainable and accurate than one broad prompt or model trying to cover disparate domains.
NEW QUESTION # 116
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 # 117
You deploy an AI application across multiple Azure regions.
The application must be able to view writes across all regions within a predictable time window.
You need to determine the appropriate consistency level.
What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
The two appropriate consistency levels for a globally distributed AI application needing to view writes within a predictable time window are Bounded Staleness and Strong.
Bounded Staleness: This provides a guarantee that reads will lag behind writes by a user- specified "staleness window" (either in time or number of operations). It delivers high availability and predictable read consistency across all regions, balancing performance with strict read-order guarantees.
Strong: This provides linearizable and absolute consistency, ensuring that writes are seen synchronously across all regions. It features an exact zero-second predictable time window, though it comes at the expense of higher latency for multi-region operations due to the time required to replicate globally.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
NEW QUESTION # 118
You need to translate real-time spoken customer conversations from English to Spanish text during a support call, with minimal latency.
Which Azure AI service should you use?
Answer: C
Explanation:
Use Azure AI Speech with Speech Translation . Microsoft documents Speech Translation as a real-time capability that accepts a live audio stream in a source language and returns translated text, synthesized speech, or both in one or more target languages. This matches the scenario precisely: the input is spoken English during a live support call, the required output is Spanish text, and latency must be minimal.
Speech Translation can be implemented through the Speech SDK , which returns intermediate recognition and translation results as speech is detected. That streaming behavior is materially better suited to live conversations than a workflow that first transcribes audio and then separately calls a text-only translation API.
Microsoft specifically positions Speech Translation for low-latency, real-time speech-to-text and speech-to- speech translation scenarios.
Azure AI Translator translates text and therefore would require a separate speech-to-text stage before translation. Azure AI Language provides NLP capabilities such as classification, sentiment, and entity extraction rather than live speech translation. Azure AI Document Intelligence extracts structured information from documents and forms and is unrelated to streaming conversational audio.
Therefore, Azure AI Speech (Speech Translation) is the correct choice.
Study Guide references: Azure AI Speech # Speech Translation; real-time transcription and translation; Speech SDK; streaming audio translation; target-language text output.
NEW QUESTION # 119
An HTTP-triggered Azure Function must read a JSON configuration file stored in Azure Blob Storage.
You need to retrieve the configuration file contents.
What should you use?
Answer: D
Explanation:
To read a configuration file stored in Azure Blob Storage from an HTTP-triggered function, you must use an Azure Blob storage input binding.
Input bindings allow you to connect external data to your function natively without writing code to explicitly connect, authenticate, and download data using SDK clients.
Strategic Overview
The Trigger: You will use an HTTP Trigger to execute the function via a web request.
The Input: You will use a Blob Input Binding pointing to the specific JSON path in your storage account.
The Result: The Azure Functions runtime automatically retrieves the file contents and provides them directly to your function code as a string, byte array, or parsed object parameter.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-triggers-bindings
NEW QUESTION # 120
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