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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 - Selecting generative AI solutions for business needs - Foundational concepts of generative AI - Business benefits, ROI, and efficiency gains |
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Aligning Microsoft AI tools to business requirements - Microsoft 365 Copilot features and use cases - Microsoft Copilot Studio capabilities - Azure AI services and Azure AI Foundry tools |
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Organizational readiness and change management - Responsible AI principles and governance - Measuring success and continuous improvement - Adoption planning and scaling AI solutions |
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問題 #79
- Select the answer that correctly completes the sentence.
When you use Microsoft 365 Copilot connectors to connect external content to __________, your users can find, summarize, and learn from line-of-business (LOB) data by using natural language prompts.
答案：
解題說明：
Explanation:
Microsoft Graph
Microsoft 365 Copilot connectors (built on Microsoft Graph connectors) are used to bring external, line-of- business content into the Microsoft 365 ecosystem by ingesting it into Microsoft Graph . Once connected, the content can be indexed and made discoverable through Microsoft Search and available for Copilot experiences, enabling users to use natural language prompts to find and summarize relevant LOB information-subject to permissions and governance controls.
The other choices don't match how Copilot connectors are positioned. Azure AI Search is an Azure indexing
/retrieval service used in custom RAG solutions, but Microsoft 365 Copilot connectors are specifically designed to surface external content through Microsoft 365 experiences via Graph. Microsoft Purview focuses on data governance, compliance, and risk management rather than being the primary ingestion target for Copilot connector content. SharePoint can store content, but the connector model is about indexing external systems into Microsoft Graph so the content becomes searchable and usable across Microsoft 365, not merely placing it into SharePoint as the destination.
So the correct completion is Microsoft Graph because that is the foundational data and indexing fabric Copilot uses to reason over organizational content with appropriate permission trimming.
問題 #80
Your company is reviewing a new AI solution before deploying it. The company wants to ensure that the solution follows Microsoft responsible AI principles.
What is the best approach to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
答案：D
解題說明：
The correct answer is B. Testing the AI solution to identify and mitigate unfair or inconsistent outcomes directly supports Microsoft responsible AI principles, especially fairness, reliability and safety, accountability, and inclusiveness. Before deployment, organizations should evaluate whether the system behaves consistently across different user groups, avoids discriminatory outcomes, and produces safe and reliable outputs.
Automatically approving or rejecting loan applications increases risk unless strong governance and human oversight are in place. Collecting personal data can increase privacy exposure if it is not necessary and controlled. Prioritizing model performance alone is also insufficient because a technically accurate model can still produce biased, unsafe, or non-transparent outcomes. Responsible AI requires evaluation, mitigation, oversight, and monitoring.
問題 #81
Match the business scenario to the appropriate AI solution design approach. Each solution may be used once, more than once, or not at all.
答案：
解題說明：
Explanation:
* The marketing department at your company wants AI to summarize emails and create presentations.
The answer: Use Microsoft 365 Copilot
* The HR department at your company wants a conversational agent for policy questions and leave requests. Answer: Build with Microsoft Copilot Studio
* The manufacturing department at your company wants AI to predict maintenance schedules. Answer:
Build with Azure Machine Learning
* The finance department at your company wants AI-powered access to enterprise resource planning ERP data by using familiar productivity tools. Answer: Extend with Microsoft 365 Copilot connectors These scenarios map to four distinct solution patterns: out-of-the-box productivity assistance, low-code conversational agents, predictive ML, and enterprise data integration.
Marketing's need to summarize emails and create presentations is a core "productivity copilot" use case.
Microsoft 365 Copilot is embedded in Outlook, Word, PowerPoint, and Teams, so it directly supports summarization, drafting, and presentation generation without building a custom solution-making Use Microsoft 365 Copilot the best fit.
HR's requirement is a conversational agent tailored to internal policies and workflows such as leave requests.
That typically needs custom dialog, grounded knowledge sources, and possibly actions/workflows. Microsoft Copilot Studio is designed to build and manage such agents with organizational knowledge and business process integration, so Build with Microsoft Copilot Studio fits best.
Manufacturing's predictive maintenance scheduling is classic predictive analytics: learning patterns from historical telemetry/maintenance data to forecast failures or optimal service windows. This is best addressed with Azure Machine Learning , which supports training, evaluating, and deploying custom predictive models.
Finance wants AI-powered access to ERP data "using familiar productivity tools," which implies bringing external line-of-business data into the Microsoft 365 Copilot experience. That is precisely where Microsoft
365 Copilot connectors help-indexing and exposing enterprise data sources so Copilot can reference them in a governed way-so Extend with Microsoft 365 Copilot connectors is the best approach.
問題 #82
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?
答案：B
問題 #83
Select the answer that correctly completes the sentence.
答案：
解題說明：
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.
問題 #84
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