您是否感興趣想通過AI-103考試，然后開始您的高薪工作？VCESoft擁有最新研發的題庫問題及答案，可以幫助數百萬的考生通過AI-103考試并獲得認證。我們提供給您最高品質的Microsoft AI-103題庫問題及答案，覆蓋面廣，可以幫助考生進行有效的考前學習。所有購買AI-103題庫的客戶都將得到一年的免費升級服務，這讓您擁有充裕的時間來完成考試。我們會100%為您提供方便以及保障，請記住能讓您100%通過考試的題庫就是我們的Microsoft AI-103考古題。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
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問題 #96
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 #97
You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?
答案：B
解題說明：
The correct answer is A. Set the input_fidelity parameter to high . The scenario requires the generated image to preserve the identity and visual characteristics of the user-supplied product photo. In Azure OpenAI image editing and generation workflows, input_fidelity controls how strongly the model attempts to match the style and features of the input image. Microsoft's documentation states that this parameter lets you make subtle edits without changing unrelated areas, and that high input fidelity preserves input-image features more accurately than standard mode.
Including a prompt and input image is necessary for image-guided generation, but it does not by itself maximize preservation of the product's appearance. The explicit preservation control is input_fidelity, and the requirement specifically asks to maintain product identity and visual characteristics. A groundedness detection filter applies to validating generated text against source data, not preserving visual features in image generation. Lowering temperature may reduce randomness in text generation, but it is not the image-control parameter used to retain product-specific visual details. Reference topics: Azure OpenAI image generation, image edit API, input images, input_fidelity, image-to-image generation, and visual identity preservation.
問題 #98
You have a Microsoft Foundry project that contains a customer support agent built on a deployed chat model.
The agent responses are validated by using an automated testing system that compares generated answers to stored expected outputs. Identical prompts must return consistent response to prevent automated test failures.
You need to reduce response variability, without modifying the prompt or reducing factual accuracy.
What should you do for the model?
答案：A
解題說明：
To reduce response variability and ensure identical prompts return consistent answers, you should decrease the temperature parameter.
Temperature controls the randomness of the model's output. Setting the temperature closer to 0 makes the model deterministic. It forces the model to choose the highest-probability words every time, ensuring that identical prompts consistently yield identical or near-identical responses to pass your automated testing.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/runtime-components
問題 #99
You configure Azure AI Content Safety to moderate user-uploaded images in a generative application. During testing, a colleague claims an image returned a Violence severity score of 3.
Why is this result impossible?
答案：D
解題說明：
The Azure AI Content Safety image model uses a trimmed severity scale that returns only 0, 2, 4, and 6, so an odd value such as 3 cannot occur for image content. The text model uses the full 0 to 7 scale, which is where odd-numbered severities appear.
問題 #100
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
答案：C
問題 #101
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