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NEW QUESTION # 34
Your company plans to use generative AI to help project managers and engineers work with construction blueprints stored as PDF files.
You need to recommend a generative AI solution that meets the following business requirements:
- Processes both images and text
- Summarizes the design of a building
- Answers user questions about a building's design
- Extracts information from blueprints, such as the location of
electrical, heating, and plumbing systems
What should you recommend?
Answer: B
Explanation:
The decisive requirement is the first one: "Processes both images and text." Blueprints are inherently visual (floor plans, schematic diagrams, spatial layouts) and textual (labels, dimensions, annotations). A multi-modal model can ingest the PDF as imagery and text together and reason across both - which is what every one of the four requirements depends on.
Summarizing a building's design, answering questions about it, and locating electrical/heating/plumbing systems all require understanding the visual diagram, not just reading words off the page.
NEW QUESTION # 35
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
To use Microsoft 365 Copilot chat, you must have a Microsoft Copilot license.
You do not necessarily need a separate "Microsoft 365 Copilot" add-on license to use the Copilot Chat feature.
Microsoft now includes a baseline version of Copilot Chat at no additional cost for users with eligible Microsoft 365 and Office 365 subscriptions.
Here is how the licensing impacts your experience:
*-> Without a Copilot Add-on License: You can still use Copilot Chat if you have a qualifying base subscription (such as Business Standard, E3, or E5). This version is web-grounded, meaning it can answer questions using public internet data and context from your currently open file, but it cannot search through your entire organization's emails, meetings, or files.
With a Microsoft 365 Copilot License: Adding this paid license transforms Copilot into a work- grounded assistant. It gains the ability to "reason" across your entire Microsoft Graph-searching your private inbox, calendar, and SharePoint documents to provide context-aware answers.
Box 2: Yes
Yes - Microsoft 365 Copilot chat provides context-aware assistance in Microsoft 365 apps.
Microsoft 365 Copilot chat is an AI-powered, context-aware assistant embedded directly into Microsoft 365 apps (Word, Excel, PowerPoint, Outlook, OneNote) to streamline workflow, summarize content, and create documents. It operates within a secure environment, using organizational data-including emails, chats, and files-to provide tailored assistance, with or without a separate paid add-on license.
Key Features and Capabilities:
*-> Context-Aware Assistance: Interacts with the user's active files (e.g., summarizing an open Word document) and Microsoft Graph data to provide relevant, in-the-moment help.
Integrated Apps: Available in a side pane in Word, Excel, PowerPoint, Outlook, and OneNote.
Content Generation & Summarization: Helps draft content, revise tones, summarize long email threads, and analyze data.
Secure Data Usage: Built-in with enterprise-grade data protection, ensuring that user data remains secure and is not used to train public models.
Functionality: Capabilities include uploading images, expanding input boxes, and providing quick access to agents and page-creation tools.
Box 3: No
No - Microsoft 365 Copilot chat can only access information in open files and read emails.
While Copilot does work with active, open content, it is designed to ground its responses in a much broader range of organizational data. It uses the Microsoft Graph to access, search, and summarize data across your entire Microsoft 365 tenant, provided you have the necessary permissions.
Reference:
https://www.microsoft.com/en-us/microsoft-365-copilot/pricing/enterprise
https://support.microsoft.com/en-gb/topic/how-copilot-chat-works-with-and-without-a-microsoft-365-copilot-license-5810b659-fbe0-48ee-9fe6-d731fe86cdeb
https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-privacy
NEW QUESTION # 36
Which statement accurately describes the difference between a pretrained generative AI model and a fine-tuned generative AI model?
Answer: A
Explanation:
Pretrained generative AI models are trained on massive, diverse datasets to gain foundational knowledge, while fine-tuned models take these pretrained weights and further train them on smaller, specific datasets to improve accuracy for narrow tasks or industries. This process aligns the model's output to specialized styles, domains, or tasks.
Key Differences and Details:
Pretrained Models (Foundational): These models (e.g., GPT-4) learn general language, concepts, and patterns from massive, broad datasets like Common Crawl. They are versatile but may lack expertise in specialized fields.
Fine-tuned Models: By adjusting the weights of a pretrained model on a smaller, labeled dataset, the model is tailored to specific applications, such as medical analysis, legal document review, or a particular brand voice.
Performance Benefits: Fine-tuning improves precision and reduces irrelevant outputs compared to a generic model.
Methodology: While pretraining is unsupervised or self-supervised, fine-tuning often uses supervised learning Reference:
https://www.ibm.com/think/topics/fine-tuning
NEW QUESTION # 37
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
* A generative AI solution is well-suited to predict next-quarter sales trends. Answer: No
* A generative AI solution can summarize lengthy policy documents. Answer: Yes
* A generative AI solution can create product descriptions from product specifications. Answer: Yes
* No - Predicting next-quarter sales trends is primarily a forecasting/predictive analytics problem.
Microsoft differentiates predictive AI (forecasting outcomes from historical patterns) from generative AI (creating content like text, images, or code). While you can use LLMs to assist analysts (explain trends, draft narratives), the core forecasting model is typically traditional ML/time-series methods rather than generative AI as the main engine.
* Yes - Summarization is a classic, high-value generative AI capability. Given a long policy, an LLM can compress it into executive summaries, key obligations, risks, and action items, often with formatting constraints (bullets, sections, "do/don't" lists). Microsoft highlights summarization and analysis as common generative AI use cases in business contexts.
* Yes - Generative AI is well-suited to transform structured inputs (features/specs) into natural- language outputs (product descriptions). This is straightforward "content generation," where you control tone, length, and required fields (benefits, differentiators, disclaimers). Microsoft also points to generating product descriptions and similar marketing/customer-facing text as a practical generative AI scenario.
NEW QUESTION # 38
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Generative AI is designed to create new outputs, such as text, images, code, summaries, emails, product descriptions, and conversational responses, based on patterns learned from training data and the user's prompt. Predictive AI is different because it focuses on forecasting outcomes, classifying data, or estimating probabilities from historical patterns, such as predicting customer churn, sales demand, or equipment failure.
Generative AI does not necessarily use a smaller dataset, and it does not automatically produce more accurate results than predictive AI. Accuracy depends on data quality, grounding, model design, and validation. The defining distinction is content creation: generative AI produces new material, while predictive AI predicts likely outcomes.
NEW QUESTION # 39
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