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Microsoft AB-731시험은 Fast2test 에서 출시한Microsoft AB-731덤프로 도전하시면 됩니다. Microsoft AB-731 덤프를 페펙트하게 공부하시면 시험을 한번에 패스할수 있습니다. 구매후 일년무료 업데이트 서비스를 제공해드리기에Microsoft AB-731시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20-25% | - Align an AI strategy with Microsoft responsible AI policies
|
| Topic 2: Identify the business value of generative AI solutions | 35-40% | - Identify the foundational concepts of generative AI
|
| Topic 3: Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35-40% | - Identify benefits and capabilities of Microsoft 365 Copilot and Microsoft Copilot
|
Microsoft인증 AB-731시험을 패스하여 자격증을 취득하는게 꿈이라구요? Fast2test에서 고객님의Microsoft인증 AB-731시험패스꿈을 이루어지게 지켜드립니다. Fast2test의 Microsoft인증 AB-731덤프는 가장 최신시험에 대비하여 만들어진 공부자료로서 시험패스는 한방에 끝내줍니다.
질문 # 33
Your company uses Microsoft 365 Copilot.
You identify several business processes that require custom workflows and specialized automation.
You need to recommend a solution that extends Copilot capabilities while minimizing development effort and costs.
What should you recommend?
정답：D
설명：
To extend Microsoft 365 Copilot while minimizing development effort and costs for business processes requiring specialized automation, you should build a declarative agent by using the
"lite" experience of Microsoft Copilot Studio (often referred to as the Agent Builder).
Declarative Agent (Lite Experience)
Best for: Minimizing effort and cost while staying within the Microsoft 365 ecosystem.
Effort: Low; utilizes a natural language interface where you describe tasks and workflows in plain language.
Capabilities: Adds custom instructions, knowledge (like SharePoint files), and specific actions (API plugins) to the existing Copilot orchestrator.
Cost: Often included with Microsoft 365 Copilot licenses, reducing additional procurement friction.
Incorrect:
[Not A]
Agent with Microsoft Foundry
Best for: Pro-code development, specialized model tuning, and high-scale, multi-agent orchestration.
Effort: High; requires professional developers, data scientists, and specialized skills in languages like Python or C#.
Cost: Higher due to specialized developer resources and Azure consumption-based pricing for models and infrastructure.
[Not C]
Custom Agent (Full Copilot Studio)
Best for: More complex, branched workflows and departmental-scale automation.
Effort: Moderate; involves a low-code, drag-and-drop interface and may require understanding
"Topics" and manual conversation logic.
Capabilities: Offers greater reach, bespoke integrations, and can be published as a standalone bot beyond Microsoft 365 apps.
Reference:
https://peafowlit.com/blog/microsoft-copilot-studio-vs-foundry-ai-agents
질문 # 34
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.
정답：
설명：
Explanation:
Box 1: No
No - A generative AI solution is well-suited to predict next-quarter sales trends.
Predictive AI, not generative AI, is designed to forecast future trends and outcomes.
While Generative AI (GenAI) and Predictive AI both use historical data and machine learning, their primary purposes are distinct: Generative AI is designed to create new, original content (text, images, code), whereas Predictive AI is designed to forecast future trends and outcomes.
Box 2: Yes
Yes - A generative AI solution is well-suited to summarize lengthy policy documents.
Generative AI solutions are exceptionally well-suited to summarize lengthy policy documents, offering a transformative approach to document management that enhances efficiency, reduces manual labor, and improves accuracy. These systems can analyze hundreds of pages in minutes, extracting key obligations, deadlines, and critical information while removing redundancy.
Box 3: Yes
Yes - A generative AI solution can create product descriptions from product specifications.
Generative AI solutions significantly streamline e-commerce by transforming raw product specifications-such as materials, dimensions, and technical features-into engaging, human- readable product descriptions. By leveraging Large Language Models (LLMs) and Natural Language Processing (NLP), these tools can automate content creation for thousands of items in minutes, ensuring consistency in brand voice and improving search engine optimization (SEO) Reference:
https://nfina.com/generative-ai-vs-predictive-ai/
https://artificio.ai/blog/generative-ai-for-document-summarization-and-insights
https://describely.ai/blog/ai-generated-product-descriptions-for-ecommerce
질문 # 35
Which business requirement most closely relates to grounding a generative AI model?
정답：B
질문 # 36
Hotspot Question
Select the answer that correctly completes the sentence.
정답：
설명：
Explanation:
Box: uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot _______________.
The Analyst agent in Microsoft 365 Copilot is a specialized, AI-powered "virtual data scientist" designed to transform raw, structured data into actionable insights, utilizing text, tables, charts, and graphs to present findings.
It is designed to work with structured data sources, including Excel files, CSVs, tables, and databases, to help users analyze data without needing advanced data science expertise.
Reference:
https://blog.storyals.com/sv/meet-your-new-ai-teammates-researcher-analyst
질문 # 37
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?
정답：C
설명：
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.
질문 # 38
......
성공을 위해 길을 찾고 실패를 위해 구실을 찾지 않는다는 말이 있습니다. Microsoft인증 AB-731시험이 영어로 출제되어 시험패스가 너무 어렵다 혹은 회사다니느라 공부할 시간이 없다는 등등은 모두 공부하기싫은 구실에 불과합니다. Fast2test의 Microsoft인증 AB-731덤프만 마련하면 실패를 성공으로 바꿀수 있는 기적을 체험할수 있습니다.제일 간단한 방법으로 가장 어려운 문제를 해결해드리는것이Fast2test의 취지입니다.
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