최신버전AB-731적중율높은인증덤프덤프자료

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Microsoft AB-731 시험요강:

주제소개
주제 1
  • Identify the Business Value of Generative AI Solutions: Covers core generative AI concepts, cost drivers, and business challenges, along with techniques like prompt engineering and RAG that enhance AI value through better data quality, security, and machine learning practices.
주제 2
  • Identify an Implementation and Adoption Strategy for Microsoft's AI Apps and Services: Covers responsible AI principles, governance, and organizational adoption planning, including AI councils, champion programs, and an understanding of Copilot and Azure AI licensing models.
주제 3
  • Identify Benefits, Capabilities, and Opportunities for Microsoft's AI Apps and Services: Focuses on mapping Microsoft's AI ecosystem including Microsoft 365 Copilot, Copilot Studio, and Azure AI Foundry Tools to real business use cases, while leveraging built-in scalability, security, and safety benefits.

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Microsoft AB-731최신버전 덤프문제 & AB-731유효한 최신덤프

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최신 Agentic AI Business Solutions Architect AB-731 무료샘플문제 (Q39-Q44):

질문 # 39
- Select the answer that correctly completes the sentence.
The primary goal of generative AI is __________.

정답：

설명：

Explanation:
to create new content, such as text, images, or code.
Generative AI is defined by its ability to produce new outputs -content that did not previously exist in exactly that form-based on patterns learned from large datasets. That content can be text (emails, summaries, policies), images (design mockups, marketing visuals), code (snippets, scripts), audio, and more. Therefore, the correct completion is "to create new content, such as text, images, or code." The other options describe different AI categories. "Analyze trends and classify data sources" is primarily analytical/classification work, typically associated with traditional machine learning models (for example, clustering, categorization, fraud classification). "Make predictions based on historical data" is predictive AI (forecasting demand, predicting churn, estimating failure probability). While generative AI can assist those workflows by explaining results or drafting narratives, its primary purpose is not classification or forecasting-it is content synthesis.
In practical business value terms, this is why generative AI is commonly deployed for productivity tasks like drafting and rewriting content, summarizing long documents, generating customer communications, creating knowledge assistants, and producing structured outputs (tables, bullet lists, JSON) from unstructured prompts.
The model's differentiator is its ability to transform instructions and context into coherent, human-like content.


질문 # 40
- Select the answer that correctly completes the sentence.
Using high-quality grounding data in a generative AI solution __________.

정답：

설명：

Explanation:
improves the accuracy and reliability of the predictions and outputs of AI.
High-quality grounding data improves a generative AI solution by anchoring responses to trusted, relevant, and up-to-date information , which increases the likelihood that outputs are accurate, consistent, and aligned with the organization's expectations. This is why the best completion is " improves the accuracy and reliability of the predictions and outputs of AI ." When the model is given authoritative context (for example, approved policy text, product specifications, knowledge base articles, or controlled enterprise content), it has less need to "guess" based on general patterns in its training data. That reduces hallucinations and improves response relevance to the user's question and the business domain.
It does not "ensure that all responses are factually accurate" because grounding reduces errors but cannot eliminate them completely-retrieval can return incomplete or irrelevant passages, user prompts can be ambiguous, and the model can still misinterpret context. It also does not inherently "increase performance of an AI model" in the sense of speed/throughput or model capability; grounding is an architecture and data strategy that improves output quality, not compute efficiency. Finally, grounding is not about "increasing storage required to host an AI model." While you may store documents in an index or repository, the core benefit is improved response quality through better context, not larger model hosting requirements.


질문 # 41
Your company receives thousands of scanned invoices each month.
You need to recommend an AI solution that can automatically extract key details, such as invoice numbers, vendor names, and total amounts.
What is the best solution to recommend? More than one answer choice may achieve the goal.
Select the BEST answer.

정답：D


질문 # 42
What is considered a best practice when forming an AI adoption team in an enterprise environment?

정답：A

설명：
Forming a cross-functional AI adoption team is a foundational best practice for enterprise environments.
A diverse "AI Center of Excellence" (CoE) or steering committee ensures that technical capabilities do not develop in isolation from regulatory requirements or business goals.
Key Representatives & Their Roles
*-> Executive Leadership: Champions the vision, secures budget, and ensures the AI strategy aligns with high-level corporate priorities.
*-> Legal & Compliance: Manages risk related to data privacy (e.g., GDPR), intellectual property, and evolving AI regulations to maintain stakeholder trust.
*- Business Units: Identify high-value use cases, define success metrics (KPIs), and ensure the AI tools actually solve operational pain points.
IT & Data Science: Provides the technical architecture, manages data pipelines, and handles the actual deployment and monitoring of models.
Change Management: Focuses on the "human" side of adoption, including upskilling employees and addressing fears about job displacement.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/center-of- excellence


질문 # 43
You plan to meet with stakeholders to discuss how generative AI can benefit your company. You need to provide a relevant description of generative AI. Which description should you use?

정답：D

설명：
Generative AI's defining capability is producing new content (text, images, code) in response to instructions-most commonly provided as natural language prompts. Option A best captures that general- purpose description for stakeholders: users ask questions or provide instructions, and the system generates responses or drafts content accordingly.
B is a specific application (translation) that generative AI can do, but it's not the defining description. C describes predictive analytics/forecasting, which is a different AI category. D describes recommendation systems, typically driven by user behavior and ranking algorithms, which can be enhanced by AI but is not the core definition of generative AI.


질문 # 44
......

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