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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
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問題 #60
Hotspot Question
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition (OCR). The solution must meet the following requirements:
- Use a single key and endpoint to access multiple services.
- Consolidate billing for future services that you might use.
- Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: PUT
Need to create a Cognitive Services Multi-Service resource using the PUT method. This specific resource type provides a single endpoint and key for multiple AI services, consolidates billing, and supports Azure Vision.
Box 2: CognitiveServices
Using the CognitiveServices kind creates a multi-service resource. This fulfills all the requirements by providing a single key and endpoint for multiple services, consolidating billing, and enabling access to features like Azure AI Vision and Text Analytics under one roof.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/sdk-overview
問題 #61
You have a Microsoft Foundry project that contains an agent.
You need to enable long-term memory to ensure that the agent can recall user preferences across separate conversations. Stored memories must be isolated per authenticated user without the client application manually generating user IDs.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
scope = " {{userId}} "
tools = [memory_tool]
The correct scope value is {{userId}} because the requirement is per-authenticated-user memory isolation without the client application manually generating user identifiers. In Microsoft Foundry Agent Service memory, the scope parameter partitions memory items inside the memory store. The official guidance states that when the memory search tool is attached to an agent, setting scope to the user identity template enables per-user memory isolation; the service resolves the end-user identity from the request header when provided, or falls back to the Microsoft Entra tenant ID and object ID of the caller. This matches the requirement to isolate stored preferences by authenticated user automatically.
The tools property must be [memory_tool] because the MemorySearchTool instance is created earlier and must be attached to the PromptAgentDefinition. Foundry guidance shows the memory search tool being passed in the agent definition as tools=[tool] , allowing the agent to read from and write to the configured memory store during conversations.
" session " and {{conversationId}} would limit continuity to a session or conversation instead of enabling long-term recall across separate conversations. [mem_store_name] is a list containing the store name, not a tool definition. Reference topics: Foundry Agent Service memory, memory stores, memory search tools, scope, and per-user isolation.
問題 #62
You have an Azure AI agent solution.
You plan to create an agent-based app named App1 that will analyze and summarize data for users and generate data-driven recommendations. App1 will be used by non-technical business users and must adapt to new and unforeseen business challenges and improve its performance over time.
You need to identity which type of agent to use in App1. The solution must meet the following requirements:
- Adapt and improve the agents' performance over time based on user
feedback.
- Provide tailored recommendations to help users make informed
decisions.
- Provide the best possible performance of the app.
Which agent type should you identify?
答案：C
解題說明：
A cognitive agent is designed to mimic human thought processes, making it ideal for the complex, data-driven tasks described in the query.
Analyze and summarize data and generate data-driven recommendations:
Cognitive agents use machine learning and natural language processing to analyze historical data and act on their learnings, which includes generating insights and recommendations.
Adapt and improve over time based on user feedback:
They are designed to learn and adapt to user behavior and preferences over time, improving their responses and performance through feedback.
Used by non-technical business users:
These agents can be accessed through user-friendly interfaces, often natural language-based (like virtual assistants), making them suitable for non-technical users.
Provide tailored recommendations to help users make informed decisions:
By learning from data and interactions, they can deliver personalized recommendations that support better decision-making.
問題 #63
Hotspot Question
You have a Python application collects customer comments before posting them to a public forum.
You need to send a text comment to Azure AI Content Safety and return the self-harm severity from the response.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: AnalyzeTextOptions(text=comment)
To set up the request in your Python script, you must use AnalyzeTextOptions(text=comment), passing the text as a single string rather than a list.
Example:
Build the request options focusing on Text analysis
# You can optionally restrict analysis to specific categories
request_options = AnalyzeTextOptions(
text=user_comment,
categories=[TextCategory.SELF_HARM]
Box 2: client.analyze_text(request)
You must use client.analyze_text(request)
1. Send the request: Call client.analyze_text(request) to retrieve the multi-severity results.
2. Extract the self-harm result: Locate the self-harm categories from the response list.
3. Return the severity: Access the .severity attribute
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/ai-contentsafety-readme
問題 #64
You have a Semantic Kernel agent that provides answers to product questions.
You plan to extend the capabilities of the agent to answer questions about the status of orders.
You need to ensure that the agent answers order status questions as quickly as possible. The solution must minimize development effort.
What should you include in the solution?
答案：A
解題說明：
To enable the agent to answer questions about order status, you need a plugin.
In Semantic Kernel, a plugin acts as a container for the specific capabilities (functions) that allow an agent to interact with external data or systems, such as an order database or API.
To help me give you a more specific example or guide, could you tell me:
The data source where order info is kept (e.g., a SQL database, a REST API, or Shopify) The programming language you're using for the Kernel (C# or Python) Reference:
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-functions
問題 #65
......
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