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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
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NEW QUESTION # 140
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
Answer: D
Explanation:
The correct recommendation is B. Add a connection to the Azure AI Search resource . Microsoft Foundry project connections are used to centrally define access from a project to external resources, including Azure AI Search. The official connection guidance states that you can add a connection by selecting an external service such as Azure AI Search , choosing the resource, and selecting the authentication method for that resource. This creates a reusable project-level configuration rather than requiring each agent to store or duplicate search credentials independently.
For Foundry agents that use Azure AI Search, the Azure AI Search tool requires a project_connection_id, which is the resource ID of the project connection to Azure AI Search. This allows multiple agents to reference the same managed connection while using the configured endpoint and authentication settings consistently. RBAC may be part of a keyless authentication design, but it does not by itself create a centrally managed Foundry credential configuration. Disabling key-based access improves security posture but does not connect the agents. A managed private endpoint addresses network isolation, not credential centralization.
Reference topics: Microsoft Foundry project connections, Azure AI Search tool, project connection IDs, authentication, and agent grounding.
NEW QUESTION # 141
Hotspot Question
You have a Microsoft Foundry project.
- You need to create a customer support agent that meets the following
requirements:
- Grounds responses only in company policy documents stored in curated
repositories
- Retains customer preferences across separate chat sessions
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Configure retrieval from approved data sources
You should configure retrieval from approved data sources to meet this requirement.
In Microsoft Foundry (and broader Azure AI Foundry / Azure OpenAI architectures), grounding an agent exclusively in company policy documents requires implementing a Retrieval-Augmented Generation (RAG) pattern. This ensures the AI model only answers using the provided context and does not hallucinate or rely on its public training data.
Box 2: Enable agent memory that uses persistent storage
To retain user preferences across completely separate chat sessions or conversations, you must use persistent agent memory. Traditional chat history only tracks messages within a single active thread or session. By contrast, the Memory Store feature provides a managed, long-term memory system that structurally indexes user preferences and facts across different devices and separate sessions.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-capabilities-for-azure-openai-on-your-data/4144636
NEW QUESTION # 142
Drag and Drop Question
You have a web app that uses Azure AI Search.
When reviewing activity you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
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.
Answer:
Explanation:
Explanation:
Enforces Read-Only Permissions: Query keys are specifically designed to provide read-only access to the documents collection of an index. Admin keys provide full read-write administrative privileges and should never be distributed to consumer-facing applications.
Zero Downtime: Azure AI Search lets you generate up to 50 individual query keys. Creating a new one allows the app to stay online throughout the entire key rotation process Reference:
https://learn.microsoft.com/en-us/azure/search/search-security-api-keys
NEW QUESTION # 143
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
Answer: B
Explanation:
The correct answer is B. Use real-time speech to text to process streaming audio input . The scenario requires live transcription from a continuous telephony stream, with transcript text appearing within a few seconds while the call is still in progress. Azure Speech in Foundry Tools real-time speech recognition is specifically intended for immediate transcription scenarios such as call center assistance, dictation, and live meeting captioning. Microsoft's Speech guidance describes real-time speech to text as processing audio input and returning transcriptions in real time, which matches the supervisor monitoring requirement.
Batch transcription is inappropriate because it processes stored audio files after recording, not an active live stream. Speech translation is used when the primary goal is translating speech into another language, not simply producing live same-language call transcripts. Text to speech performs the reverse operation by generating spoken audio from text and does not transcribe inbound calls. Real-time speech to text provides the low-latency streaming recognition path required for live operational monitoring. Reference topics: Azure Speech in Foundry Tools, real-time speech recognition, streaming audio input, call center transcription, and live captions.
NEW QUESTION # 144
You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
To implement end-to-end tracing for a Microsoft Foundry prompt agent invoked from an external Python service, you should use a combination of Azure Application Insights, the Azure AI Projects SDK, and OpenTelemetry.
Core Components
Azure Application Insights: This serves as the centralized telemetry store. You must connect an Application Insights resource to your Foundry project via the Tracing tab in the Foundry portal to enable ingestion.
Azure AI Projects SDK (azure-ai-projects): Used in your Python service to create an AIProjectClient, which authenticates and communicates with the Foundry agent.
OpenTelemetry Python SDK: Foundry tracing is built on the OpenTelemetry standard. You will need opentelemetry-sdk and azure-monitor-opentelemetry to instrument your external service.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/observability/how-to/trace-agent-setup
NEW QUESTION # 145
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