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| Section | Weight | Objectives |
|---|---|---|
| Identify the business value of generative AI solutions | 35–40% | - Differences between generative AI and other AI types - Business benefits, ROI, and efficiency gains - Selecting generative AI solutions for business needs - Foundational concepts of generative AI |
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Azure AI services and Azure AI Foundry tools - Microsoft 365 Copilot features and use cases - Aligning Microsoft AI tools to business requirements - Microsoft Copilot Studio capabilities |
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Adoption planning and scaling AI solutions - Responsible AI principles and governance - Organizational readiness and change management - Measuring success and continuous improvement |
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NEW QUESTION # 87
Your company sells hiking and camping gear online. You need a generative AI solution that can interact with customers and ask questions about their needs. What should you include in the solution?
Answer: B
Explanation:
The requirement is an interactive generative AI experience that can converse with customers and ask clarifying questions (for example: "What climate are you hiking in?", "How many people will share the tent?", "What's your budget?", "Do you prioritize weight or comfort?"). The best solution component for that conversational, question-and-answer interaction is a chatbot (A), powered by a generative AI model.
A chatbot provides the dialog framework: maintaining conversational context across turns, prompting the user for missing requirements, and responding in natural language. This makes it suitable for customer support, guided shopping assistance, troubleshooting, and pre-sales Q & A-especially when customers don't know exactly what they need and benefit from a guided conversation.
The other options don't match the core requirement. Predictive AI (B) forecasts outcomes (like demand or churn) and isn't inherently conversational. Computer vision (C) analyzes images (like recognizing products from photos) and doesn't address asking questions in dialogue. A recommendation engine (D) can be useful in ecommerce, but it typically suggests items based on behavior or attributes; it doesn't by itself provide a conversational flow that asks users questions and adapts responses in natural language. In practice, you can combine a chatbot with a recommendation engine behind the scenes-but the "include in the solution" component that directly satisfies interactive questioning is the chatbot.
NEW QUESTION # 88
Your company is developing an AI-powered customer support agent. You need to ensure that the solution follows Microsoft responsible AI principles. Which two actions should you perform? Select the two BEST answers. Each correct answer presents part of the solution.
Answer: A,C
Explanation:
To align an AI customer support agent with Microsoft's Responsible AI principles, two high-impact actions are fairness/inclusiveness validation and transparency to users . B is correct because testing for inclusive and culturally sensitive responses directly supports fairness and helps reduce harm. In practice, you evaluate responses across diverse user personas, languages/dialects, accessibility scenarios, and sensitive contexts. You look for biased assumptions, stereotyping, exclusionary language, and disparate quality of service. This also implies ongoing monitoring because model behavior can drift as prompts, knowledge sources, and user inputs evolve.
E is correct because a clear disclaimer supports transparency: customers should know they are interacting with an AI system, understand the type of assistance it can provide, and know what to do if the response is incorrect or they need a human. A disclosure is also a practical risk-control that reduces overreliance and sets expectations about limitations.
The other options are not best for Responsible AI alignment: A (retain all conversations) can conflict with privacy/data minimization; retention must be justified and governed, not automatic. C (operate independently) undermines accountability and human oversight. D (multiple purposes) increases scope and risk rather than improving responsible use.
NEW QUESTION # 89
- 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 enables users to search across emails, files, chats, meetings, and connected apps, by using natural language queries. Answer: Yes
* Microsoft 365 Copilot provides data protection, and helps ensure that user prompts and chat data are secure. Answer: Yes
* Microsoft 365 Copilot is included in all Microsoft 365 subscriptions. Answer: No
* Yes - Microsoft 365 Copilot is grounded in Microsoft 365 content through Microsoft Graph and is designed to help users find and summarize information across their work context (emails, files, chats, meetings). With extensions/connectors, Copilot can also incorporate information from connected apps, allowing users to query using natural language and get summarized, actionable outputs.
* Yes - A key enterprise benefit of Microsoft 365 Copilot is that it operates within the Microsoft 365 security, identity, and compliance boundary. It respects existing permissions (users only get content they are authorized to access) and supports organizational controls intended to protect prompts and generated content. This is part of the business value compared to consumer tools: IT can apply policies, auditing, and compliance controls to reduce data risk.
* No - Microsoft 365 Copilot is not automatically included in every Microsoft 365 subscription. In most business scenarios it is licensed as an add-on (or separate Copilot entitlement) for eligible Microsoft
365 plans, meaning it requires explicit licensing rather than being universally bundled with all subscriptions.
NEW QUESTION # 90
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 # 91
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: Yes
Yes - A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
A manufacturer can use Azure Vision in Foundry Tools (formerly part of Azure AI Services, now integrated within the AI Foundry toolkit) to identify product defects on an assembly line. This solution automates visual inspections to detect anomalies such as surface scratches, cracks, misalignments, or missing components in real time.
Box 2: Yes
Yes - A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
A logistics company can use Azure Vision in Foundry Tools (part of the broader Azure AI services suite) to recognize, interpret, and digitize package shipping labels. By integrating Azure's advanced AI with Palantir Foundry, firms can automate manual data entry, track shipments, and improve operational efficiency.
Box 3: No
No - The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files.
Azure Vision in Foundry Tools is primarily designed for images, while its sibling tool, Document Intelligence, handles Microsoft Word files.
Reference:
https://datalabs.io/azure-ai-for-smart-manufacturing-defect-detection-with-computer-vision
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-ocr
NEW QUESTION # 92
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