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| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
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NEW QUESTION # 66
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.
Answer:
Explanation:
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.
NEW QUESTION # 67
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 # 68
You are deploying a support agent that enables users to upload photos.
You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
What should you do?
Answer: B
Explanation:
The best solution in this scenario is to implement image moderation.
Azure AI Content Safety provides dedicated image moderation capabilities that automatically detect, classify, and score harmful visual content (such as violence, hate speech, sexual content, and self-harm). It natively allows you to configure threshold blocklists based on four severity levels (Low, Medium, High), perfectly matching the requirements.
Incorrect:
[Not A]
Optical Character Recognition (OCR) only extracts text embedded within an image. If a user uploads a harmful photo that contains no text (e.g., a violent image), OCR and keyword scanning will completely fail to detect it.
[Not B]
The Prompt shields feature is designed to protect generative AI models from text-based attacks, such as jailbreaking or prompt injection. It does not analyze or classify the visual pixels of an uploaded photo for harmful content.
[Not C]
While Azure AI Content Safety uses blocklists, standard text blocklists match specific exact terms or regex patterns. They are used for text moderation, not for evaluating the severity levels of raw visual imagery.
Reference:
https://supportzebra.com/blog/what-is-ai-image-moderation
NEW QUESTION # 69
A multimodal agent processes user-uploaded images. An attacker embeds hidden text instructions inside an image, hoping the agent will read and act on them. Which Azure AI Content Safety capability is designed to detect this kind of attack?
Answer: B
Explanation:
Prompt Shields detects prompt injection attacks, including indirect injection where malicious instructions are embedded in content the model processes, such as text hidden inside an image.
NEW QUESTION # 70
You process vendor onboarding PDFs containing tables and hyperlinks. The extracted content is indexed and supplied to an agent in Markdown.
You need Markdown with layout and semantic structure optimized for RAG.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use prebuilt-documentSearch . Microsoft ' s current Content Understanding documentation explicitly classifies prebuilt-documentSearch as a Retrieval Augmented Generation analyzer .
RAG analyzers extract content with layout represented as Markdown and perform semantic analysis to enhance retrieval quality for downstream applications. prebuilt- documentSearch extracts document elements including paragraphs, tables, and figures and produces layout-preserving structured Markdown optimized for indexing and retrieval. Microsoft Learn prebuilt-layout is useful when the principal requirement is document-layout extraction, but the question explicitly requires both layout and semantic structure optimized for RAG . That wording maps directly to the RAG analyzer rather than the base layout analyzer.
prebuilt-read primarily addresses basic textual extraction, while prebuilt-documentFieldSchema serves field
/schema-oriented scenarios rather than producing RAG-optimized document representations.
Therefore, although the supplied file displays C (prebuilt-layout) , the verified current Microsoft Learn answer is B (prebuilt-documentSearch) . Microsoft ' s SDK documentation also describes prebuilt- documentSearch as preserving layout, detecting tables and figures, and generating structured Markdown specifically for RAG. Microsoft Learn
NEW QUESTION # 71
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