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NEW QUESTION # 81
You are developing an application named App1 that analyzes customer feedback.
App1 will call Azure Language in Foundry Tools from a generative AI agent in Microsoft Foundry.
You need to ensure that the agent can access Azure Language text analysis.
What should you configure?
Answer: A
Explanation:
The Azure Language MCP (Model Context Protocol) server needs to be configured so your generative AI agent can utilize text analysis tools (like sentiment analysis, keyphrase extraction, and language detection).Azure Language in Foundry Tools exposes its capabilities as standardized tools using the MCP framework. This means your agent cannot call the traditional REST API directly; it must route requests through an MCP server configuration.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview
NEW QUESTION # 82
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
No, an Azure Content Understanding analyzer does not return results in HTML format. Instead, it processes your input files (which can include HTML) and structures the extracted output into Markdown, JSON fields, and segments.
Box 2: No
Only if you use the newly released synchronous operations introduced in the 2026-06-01-preview API version. If you are using the standard or generally available (GA) versions of the API, the process runs asynchronously by default.
Box 3: Yes
Yes, Azure Content Understanding in Foundry Tools can ingest and extract structured information from documents, images, audio, and video files Multimodal Ingestion: Processes unstructured data across text, images, audio recordings, and video files into user-defined formats like JSON or Markdown.
Schema-Driven Extraction: Uses prebuilt or custom schemas to extract, classify, or generate specific fields without complex prompt engineering.
Confidence & Grounding: Returns confidence scores and source grounding to verify accuracy and minimize manual oversight.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/analyzer-reference
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/whats-new
NEW QUESTION # 83
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models, not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.
NEW QUESTION # 84
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
AI capabilities depend heavily on their training and architecture. While modern multimodal models handle both text and visual data, many specialized AI systems perform only one specific task or work exclusively with text, numbers, or audio.
Box 2: Yes
Vision-enabled generative AI models can absolutely extract text from images. These systems- often called Vision-Language Models (VLMs) or multimodal models-combine visual processing with language capabilities to read, understand, and extract text seamlessly from visual media.
Box 3: Yes
A multimodal model can process prompts that include both text and images. These advanced systems use separate encoders or unified architectures to understand and connect visual details from pictures with the meaning of words in a single task.
Reference:
https://bestarion.com/vision-llms-for-image-understanding-and-text-extraction/
NEW QUESTION # 85
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Generating a response to a user prompt occurs during the inference stage. = Yes When a deployed generative AI model receives a user prompt and generates an output, that process is called inference . The model is being used to produce a response, not being trained.
Statement 2: A generative AI model generates responses by copying stored documents directly from the model's training data. = No Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.
Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model's training data. = Yes This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.
NEW QUESTION # 86
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