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NEW QUESTION # 36
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
* Retrieval Augmented Generation RAG requires model fine-tuning. Answer: No
* Retrieval Augmented Generation RAG is helpful when you need a generative AI solution that can access current, verifiable information. Answer: Yes
* Retrieval Augmented Generation RAG enables you to get more relevant responses based on your organization's documents without retraining the base model. Answer: Yes RAG is an architecture pattern that improves generative AI responses by retrieving relevant information from external knowledge sources (for example, a document index, database, or knowledge base) and injecting that information into the model's prompt/context at runtime.
* No - RAG does not inherently require fine-tuning. Fine-tuning changes the model weights. RAG, instead, keeps the base model as-is and augments it with retrieved context. Fine-tuning can be complementary (for style, domain tone, or specialized tasks), but it is not required for RAG to work.
* Yes - RAG is especially valuable when you need current and verifiable information because the retrieval layer can pull the latest approved content (updated policies, product specs, incident runbooks) and provide it to the model. This reduces hallucinations and makes answers traceable to known sources.
* Yes - A major benefit of RAG is improved relevance to organizational documents without retraining. Instead of rebuilding the model whenever documents change, you update the underlying content store/index; the model then generates responses grounded in the retrieved passages, producing answers that align with your organization's latest information and terminology.
NEW QUESTION # 37
Your company has an AI solution that uses a prebuilt Azure OpenAI model to generate content.
You need to reduce the cost of solution while minimizing the impact on the quality of the generated output.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
To reduce costs for a prebuilt Azure OpenAI model while maintaining high output quality, you should implement a combination of prompt optimization, caching, and model selection strategies.
[C]
1. Optimize Prompt Engineering
Reducing the number of tokens processed is the most direct way to lower costs.
Prompt Compression: Use tools like LLMLingua to remove redundant text from prompts without losing semantic meaning.
Task Decomposition: Break complex tasks into smaller, focused prompts to improve clarity and reduce expensive retries.
Shorten Context: Summarize long conversation histories or external RAG (Retrieval-Augmented Generation) documents before sending them to the model.
2. Implement Caching Mechanisms
[A]
3. Use Model Routing and Tiering
Matching request complexity to model capability can reduce costs by 30-60%.
*-> Model Cascading: Use a small, cheap model (e.g., GPT-4o-mini) for simple tasks like classification or basic formatting. Route only complex reasoning tasks to high-end models like GPT-4o.
Reasoning Effort Control: For newer models like GPT-5, adjust the reasoning_effort parameter to
"minimal" for straightforward tasks to save on compute.
Reference:
https://ekaivakriti.com/blog/how-to-reduce-openai-costs-by-60-percent/
NEW QUESTION # 38
Your company purchases Microsoft 365 Copilot for its sales department. The sales department needs to find and summarize information across internal documents quickly. From which two data sources can the sales department obtain results by default? (Select TWO.)
Answer: A,D
Explanation:
By default, Microsoft 365 Copilot is grounded in your organization's Microsoft 365 data through Microsoft Graph , and it respects the user's existing permissions. For "find and summarize information across internal documents," the most direct default document repositories in Microsoft 365 are SharePoint (team sites and shared libraries) and OneDrive (a user's work files). That is why C (Microsoft SharePoint) and D (Microsoft OneDrive) are the correct selections. Microsoft explicitly describes Copilot as accessing organizational content via Microsoft Graph, including user documents and related work content.
The other options are not "by default" sources. A (on-premises file share) is not automatically part of Microsoft Graph unless you integrate/migrate content or use connectors to make it discoverable in Microsoft
365 experiences. B (custom CRM) similarly requires an integration approach (for example, Microsoft 365 Copilot connectors / Graph connectors) to index and expose that data for Copilot to use. E (Microsoft Sway) is not a primary default content source for Copilot's document grounding and is not typically referenced as a core internal document repository compared to SharePoint/OneDrive.
So, for immediate, out-of-the-box internal document discovery and summarization within Microsoft 365, SharePoint + OneDrive are the default answer.
NEW QUESTION # 39
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 processes both images and text, summarizes building design, answers questions, and extracts information such as locations of electrical, heating, and plumbing systems. What should you recommend?
Answer: C
Explanation:
Construction blueprints in PDFs often contain a mix of text, symbols, linework, and diagrams . The requirements include understanding both visual layout (where systems are located) and textual annotations , producing summaries, and answering Q & A. That combination requires a multimodal generative AI approach-models that can reason over images and text together. Therefore, A is best.
OCR alone (B) can extract printed text, but it won't reliably interpret diagram geometry, symbols, or spatial relationships (e.g., "electrical riser is on the east core near gridline B-4"). Text completion (C) is too generic and doesn't address image understanding. Document summarization (D) is only one requirement (summary) and still depends on first extracting/understanding both visual and textual elements.
A multimodal solution can ingest the PDF pages as images (or rendered page images) plus extracted text, then answer questions grounded in both modalities. In practice, you may combine OCR and layout extraction with a multimodal LLM so the model can reference drawing regions, legends, callouts, and system diagrams to produce accurate explanations and field extractions.
NEW QUESTION # 40
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 # 41
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