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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35-40% | - Identify benefits and capabilities of Azure AI services
|
| Topic 2: Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20-25% | - Plan for AI adoption across the organization
|
| Topic 3: Identify the business value of generative AI solutions | 35-40% | - Identify the foundational concepts of generative AI
|
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NEW QUESTION # 31
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
The correct selections are Yes, No, Yes. Microsoft 365 Copilot helps users create, rewrite, summarize, and analyze content in Microsoft 365 apps such as Outlook, Word, Excel, PowerPoint, and Teams. Therefore, it can help compose professional emails and summarize conversations. It can also summarize recent updates across business content such as documents, spreadsheets, and presentations when the user has access to that content. The second statement is false because Copilot is not generally described as autonomously updating business spreadsheets without user control. It can assist with analysis, formulas, summaries, and suggested changes, but autonomous modification of sales data would require governed user action, permissions, and potentially workflow controls. Copilot is an assistant, not an unrestricted autonomous spreadsheet operator.
NEW QUESTION # 32
You have a historical dataset that contains 1,000 records.
You need an AI solution that can analyze the data to identify patterns and predict future outcomes.
What should you include in the solution?
Answer: C
Explanation:
The primary Microsoft AI solution designed to analyze large, historical datasets (thousands of records), identify complex patterns, and predict future outcomes is Azure Machine Learning, specifically utilizing Automated Machine Learning (AutoML).
Reference:
https://vslive.com/blogs/mshq-news-and-events/2025/04/azure-ml.aspx
NEW QUESTION # 33
Match the business scenario to the appropriate AI solution design approach. Each solution may be used once, more than once, or not at all.
Answer:
Explanation:
Explanation:
Answer Area
* The marketing department at your company wants AI to summarize emails and create presentations.
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.
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.
NEW QUESTION # 34
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 # 35
- Select the answer that correctly completes the sentence.
A __________ AI solution recognizes patterns in large and complex datasets to create new and original content.
Answer:
Explanation:
Explanation:
generative
The sentence describes an AI system that creates new, original content (for example, text, images, audio, code) by learning patterns from large datasets. That is the defining characteristic of generative AI , so
"generative" is the correct completion.
To clarify the contrasts: predictive AI primarily uses historical data to forecast outcomes or estimate probabilities (for example, predicting next-quarter sales, predicting equipment failure, or classifying whether a transaction is fraudulent). It focuses on what is likely to happen or which category something belongs to , rather than producing novel content. Prescriptive AI goes a step further by recommending actions or decisions (for example, "increase inventory by X," "route tickets to team Y," "schedule maintenance next week") often using optimization and business constraints.
Generative AI, by comparison, is focused on content synthesis . It identifies statistical patterns and relationships in training data and then generates new sequences that resemble the learned distribution while being responsive to prompts. In business use cases, that translates to drafting emails and documents, summarizing reports, generating meeting notes, creating product descriptions, producing marketing copy, and building chat-based assistants. While all three categories can "recognize patterns," only generative AI is explicitly characterized by the ability to produce new and original content , which is what the question is testing.
NEW QUESTION # 36
......
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