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NEW QUESTION # 74
Your company stores hundreds of internal business reports.
You need to recommend a generative AI solution that uses an agent to answer questions based on the content in the reports.
What should you include in the recommendation?
Answer: B
Explanation:
A RAG-based generative AI solution for hundreds of internal reports uses an agent to query a vector database, ensuring answers are grounded in proprietary data, minimizing hallucinations.
The system parses reports into embeddings, retrieves relevant chunks via semantic search, and uses an LLM to generate precise, cited answers.
Key Components & Architecture
Data Ingestion & Embedding: Convert thousands of PDFs, docs, or text files into vector embeddings stored in a vector database (e.g., Pinecone, Azure AI Search, Milvus).
Agentic Workflow: Implement an intelligent agent that decomposes complex user questions into sub-queries, searches multiple data sources, and refines answers.
Retrieval Mechanism: Use hybrid search (combining semantic and keyword search) for high accuracy in finding relevant report snippets.
Generation & Grounding: The LLM receives the prompt with retrieved content to generate answers, improving quality and reducing errors.
Incorrect:
[Not D]
The GAN can be used to improve the quality of document embeddings or to generate realistic, synthetic training data for the agent's semantic search, particularly in scenarios where data is unstructured or sparse.
NEW QUESTION # 75
Your company manages an online catalog of office supplies. You plan to use a generative AI solution to create product descriptions for your company's website. The solution must ensure descriptions can be posted immediately after creation, enable selection/inclusion of product details, and be fast and simple for non- technical staff. What is the best type of solution to use? Select the BEST answer.
Answer: A
Explanation:
The task is high-volume content generation with consistent structure and immediate publishing: product descriptions that reliably include chosen product attributes (brand, specs, materials, dimensions, use cases) and can be produced quickly by non-technical staff. The best fit is a fine-tuned LLM (D) because fine-tuning can standardize tone, format, and completeness against your catalog schema, reducing variability and minimizing manual editing before posting. With a fine-tuned model, you can strongly enforce style guidelines (length, voice, prohibited claims), and you can template prompts so staff only supply product fields and get publish-ready copy.
Option A is not best: Azure Machine Learning is excellent for predictive models but is unnecessary for straightforward text generation. B (Researcher) is optimized for multistep research across work data + web, not deterministic product copy generation. C (interactive agent) can help collect requirements, but it's more complexity than needed; the core need is consistent text generation from structured product data, which fine- tuning addresses directly while keeping user interaction simple (fill fields # generate description).
NEW QUESTION # 76
Which benefit of generative AI enables organizations to accelerate content creation across departments such as marketing, HR, and communications?
Answer: C
Explanation:
Generative AI can produce first drafts of emails, reports, job descriptions, and other business documents. This accelerates content creation and reduces manual effort across organizational functions.
Reference:
https://learn.microsoft.com/en-us/training/modules/build-effective-generative-ai-solutions- organization/1-introduction
NEW QUESTION # 77
- What should you use for each task? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Extracting structured data from forms and invoices: Answer: Azure Document Intelligence in Foundry Tools
* Summarizing written content from business reports: Answer: Azure Language in Foundry Tools
* Generating descriptive text for uploaded images: Answer: Azure Vision in Foundry Tools These three tasks align to three different Azure AI capability families: document processing, language understanding/generation, and computer vision.
* Forms and invoices are semi-structured documents where the business need is to extract specific fields (IDs, names, totals, dates) reliably into structured output. Azure Document Intelligence is designed for intelligent document processing and includes prebuilt models (such as invoices) as well as custom extraction options, making it the correct choice for structured data extraction from documents.
* Summarizing written business reports is an NLP task focused on compressing long text into key points, themes, and action items. Azure Language provides language processing capabilities (including summarization features within language service capabilities), so it is the best fit for summarization scenarios.
* Generating descriptive text for images (image captioning/description) is a computer vision task.
Azure Vision can analyze uploaded images and return descriptions/captions and other visual insights, which directly matches the requirement to produce descriptive text from images.
NEW QUESTION # 78
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:
Answer Area
* Microsoft 365 Copilot connectors enable you to index data from multiple sources to make the data available in Copilot. Answer: Yes
* You can build a custom Microsoft 365 Copilot connector when the available connectors do NOT meet your data integration requirements. Answer: Yes
* To use Microsoft 365 Copilot connectors, you need a Microsoft Copilot Studio license. Answer: No
* Yes - Microsoft 365 Copilot connectors (including synced connectors) are designed to bring external data into Microsoft Graph so it can be semantically indexed and surfaced in Microsoft 365 Copilot experiences. Microsoft explicitly states that synced connectors ingest/crawl content into Microsoft Graph where it's indexed and then available for Copilot prompts and citations.
* Yes - When Microsoft-provided connectors don't meet integration needs, organizations can create custom connectors (often referred to as Microsoft Graph connectors / custom connector development) to connect other data sources. This is a common extensibility path to index line-of-business repositories and make that content discoverable via Copilot and Microsoft Search.
* No - Using Microsoft 365 Copilot connectors does not require a Copilot Studio license. Connectors are generally configured and managed in Microsoft 365 admin/search experiences, and Microsoft's licensing guidance indicates that users can view connector data in Microsoft 365 Copilot and Microsoft Search with valid Microsoft 365/Office 365 licensing-Copilot Studio licensing is about building agents in Copilot Studio, not a prerequisite to use connectors.
NEW QUESTION # 79
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