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| Section | Weight | Objectives |
|---|---|---|
| Identify the business value of generative AI solutions | 35–40% | - AI value identification
|
| Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35–40% | - Microsoft AI ecosystem
|
| Identify an implementation and adoption strategy for Microsoft AI apps and services | 20–25% | - AI adoption strategy
|
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NEW QUESTION # 76
Which statement accurately describes the difference between a pretrained generative AI model and a fine-tuned generative AI model?
Answer: B
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 # 77
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
A text-to-image generator (like DALL-E or Midjourney) takes textual descriptions and synthesizes them into visual imagery. It does not perform language translation workflows; that task requires natural language processing (NLP) or large language models (LLMs).
Box 2: No
Predictive analytics models rely on historical data and machine learning algorithms to forecast future trends, behaviors, or numerical outcomes (such as click-through rates or future sales).
They do not create or synthesize brand-new creative artifacts like marketing text or ad copies; that is the role of Generative AI.
Box 3: Yes
Generative AI chatbots (like those built using custom LLMs or Copilot Studio) excel at maintaining dynamic, human-like context. They can handle highly personalized conversations based on user profile inputs and dynamically synthesize relevant product recommendations.
NEW QUESTION # 78
Which business requirement most closely relates to grounding a generative AI model?
Answer: A
Explanation:
Grounding in generative AI means ensuring model outputs are based on trusted, relevant information sources rather than only on the model's general training data. In a business context, grounding is about aligning responses with verified enterprise knowledge (policies, product documentation, internal procedures, approved FAQs, etc.) so the system is more accurate, consistent, and defensible. That is exactly what option D describes: "ensuring that verified company data sources are used for response generation." In Microsoft AI solution patterns, grounding is commonly achieved using retrieval-augmented generation (RAG). With RAG, the system retrieves relevant passages from approved company repositories (for example, indexed documents or knowledge bases) and supplies them as context to the model during response generation. This reduces hallucinations, improves factual correctness, and makes answers more relevant to the organization's reality-critical when AI is used for customer support, employee helpdesks, compliance guidance, or executive reporting.
The other options do not directly address grounding. A relates to localization/multilingual capability, B is a usage/telemetry metric, and C is an interaction method (natural language interface). They can all be important requirements, but none of them ensure outputs are anchored to verified company data-the core purpose of grounding.
NEW QUESTION # 79
Your company wants to ensure that AI solutions are used responsibly and align with company values and compliance requirements.
You need to establish governance principles for AI use.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents a complete solution.
Answer: C,D
Explanation:
The correct answers are A and E. A governance program should include a formal process to review AI initiatives for responsible AI alignment. This ensures that projects are assessed for fairness, privacy, security, reliability, transparency, accountability, and business risk before deployment and during operation. Defining accountability norms across business and technical teams is also essential because responsible AI cannot be owned only by engineers or data scientists. Business owners, legal, compliance, security, privacy, and technical teams must understand who approves AI use cases, who monitors outcomes, and who responds when issues occur. Letting departments use separate governance methods creates inconsistency. Focusing only on regulated or sensitive-data systems is too narrow.
NEW QUESTION # 80
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? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
With Microsoft 365 Copilot, the two primary data sources used to ground data with internal documents are:
SharePoint
OneDrive
These sources allow Copilot to access, analyze, and summarize files (such as Word documents, PDFs, Excel files, and PowerPoint presentations) stored within your organization's Microsoft 365 tenant. Other sources mentioned in the context of grounding include Microsoft Teams chat history and emails.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-studio
NEW QUESTION # 81
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