참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Microsoft AB-731 시험 문제집이 있습니다: https://drive.google.com/open?id=1rSwBMW5-WbdvxYjfYeia4ximnLBv5P6U
Microsoft인증 AB-731 시험은 최근 제일 인기있는 인증시험입니다. IT업계에 종사하시는 분들은 자격증취득으로 자신의 가치를 업그레이드할수 있습니다. Microsoft인증 AB-731 시험은 유용한 IT자격증을 취득할수 있는 시험중의 한과목입니다. Itcertkr에서 제공해드리는Microsoft인증 AB-731 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다. 덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다. 시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를Itcertkr에서 제공해드립니다. Itcertkr덤프는 선택하시면 성공을 선택한것입니다.
| Section | Weight | Objectives |
|---|---|---|
| Identify an implementation and adoption strategy for Microsoft AI apps and services | 20–25% | - AI adoption strategy
|
| Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35–40% | - Microsoft AI ecosystem
|
| Identify the business value of generative AI solutions | 35–40% | - AI value identification
|
Itcertkr는 많은 분들이 Microsoft AB-731인증시험을 응시하여 성공하도록 도와주는 사이트입니다. Itcertkr 의 AB-731덤프는 모두 엘리트한 전문가들이 만들어낸 만큼 시험문제의 적중률은 아주 높습니다. 거의 100%의 정확도를 자랑하고 있습니다. 아마 많은 유사한 사이트들도 많습니다. 이러한 사이트에서 학습가이드와 온라인서비스도 지원되고 있습니다만 Itcertkr 는 이미 이러한 AB-731 사이트를 뛰어넘은 실력으로 업계에서 우리만의 이미지를 지키고 있습니다. Itcertkr 는 정확한 문제와 답만 제공하고 또한 그 어느 사이트보다도 빠른 업데이트로 여러분의 인증시험을 안전하게 패스하도록 합니다.
질문 # 46
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.
NOTE: Each correct selection is worth one point.
정답：B,D
설명：
[B]
Under Microsoft's Responsible AI framework, this testing specifically addresses the principles of Fairness and Inclusiveness. To operationalize these, you should focus on the following testing and development areas:
Key Testing Focus Areas
*-> Cultural Sensitivity: Ensure the agent respects different values, perspectives, and international contexts to avoid causing offense or misunderstanding.
*-> Fairness and Bias Mitigation: Test the agent to ensure it treats all users equitably and does not reinforce societal stereotypes or discriminate based on protected characteristics like race, gender, or religion.
Accessibility: Validate that the agent is usable by people of all abilities, providing equal power and engagement regardless of their background.
Global Community Engagement: Involve diverse users and underserved communities in the pre- deployment validation and feedback process to identify representation gaps.
[E]
To align with Microsoft's Responsible AI principles, providing a clear disclaimer that users are interacting with an AI solution is a core requirement of the Transparency principle.
Under this principle and the Microsoft Responsible AI Standard, developers must ensure that AI systems are understandable and that users are not deceived into believing they are interacting with a human.
Reference:
https://learn.microsoft.com/en-us/legal/ai-code-of-conduct
https://www.microsoft.com/en-us/ai/principles-and-approach
질문 # 47
You are exploring how Microsoft 365 Copilot uses Microsoft Graph to deliver AI-powered experiences.
Which information in Microsoft Graph can Copilot use by default?
정답：B
질문 # 48
You are exploring how Microsoft 365 Copilot uses Microsoft Graph to deliver AI-powered experiences.
Which information in Microsoft Graph can Copilot use by default?
정답：B
설명：
Microsoft 365 Copilot is designed to work within the Microsoft 365 ecosystem and use organizational context that is already governed by Microsoft Entra ID, Microsoft 365 permissions, and compliance controls. By default, Copilot can use Microsoft Graph signals and content that exist in Microsoft 365 workloads the user already has access to-most commonly emails, files, meetings, and chats . That corresponds to A .
The key concept is permission-trimming: Copilot doesn't magically gain access to everything; it can only surface or use data that the signed-in user is permitted to access in Microsoft 365. This is what makes Copilot valuable for productivity scenarios-summarizing email threads, drafting replies, generating meeting recaps, creating documents from your files, or pulling context from Teams chats-because those artifacts are already part of daily work and already subject to tenant policies.
The other options are not "by default" Microsoft Graph content for Copilot: B (file shares) typically requires additional integration or migration into Microsoft 365 repositories or indexing via connectors; it's not inherently available. C is unrelated to Microsoft 365 tenant work data. D (public web) is not "information in Microsoft Graph"; web grounding is a separate capability and not the default Graph workload data source referenced here.
질문 # 49
Your company stores thousands of reports and documents across multiple systems.
You recommend using Azure AI Search as part of a new generative AI solution to improve information discovery.
What is a key benefit of using Azure AI Search in this scenario?
정답：A
설명：
In an environment with tens of thousands of reports and documents across multiple systems, Azure AI Search (formerly Cognitive Search) significantly improves information discovery through several core mechanisms:
*-> Natural Language & Semantic Search: Unlike traditional keyword search, it understands the intent and context behind queries. Users can ask conversational questions (e.g., "Find all contracts mentioning GDPR compliance in 2023") and receive relevant results even without exact keyword matches.
*-> Unified Multi-System Ingestion: It uses indexers to automatically pull and unify data from diverse sources such as SharePoint, Azure Blob Storage, SQL databases, and Cosmos DB into a single searchable index.
AI-Powered Content Enrichment: During indexing, it can apply cognitive skills to extract information from unstructured data. This includes:
- Optical Character Recognition (OCR) to make scanned reports searchable.
- Entity Recognition to identify and tag people, locations, and organizations.
- Key Phrase Extraction and language detection to enhance metadata.
-Hybrid Retrieval: It combines vector search (for semantic meaning) with full-text search (for specific terms like product codes or names), merging them via Reciprocal Rank Fusion (RRF) to ensure high precision and recall.
Semantic Ranking: An advanced L2 ranking layer uses deep learning models from Bing to re- order the top search results, ensuring the most contextually relevant answers appear first.
This setup is commonly used as the retrieval foundation for Retrieval-Augmented Generation (RAG), where search results are fed into Large Language Models (LLMs) like GPT-4 to provide grounded, human-like answers based on your enterprise data.
Reference:
https://azure.microsoft.com/en-us/products/ai-services/ai-search
질문 # 50
You have a large language model (LLM) that was pretrained by using public data.
You want the LLM to generate responses that incorporate frequently updated proprietary content, such as internal documents and knowledge bases.
You need to recommend a solution to ensure that the LLM uses the most current information without retraining the model.
What should you include in the recommendation?
정답：C
설명：
Retrieval-Augmented Generation (RAG) is the optimal, industry-standard approach for incorporating frequently updated proprietary data into an LLM's output. It works by connecting a pre-trained LLM to an external, dynamic knowledge base--such as company documents--using vector databases to retrieve relevant context in real-time, reducing hallucinations and eliminating the need for constant, costly retraining.
Reference:
https://shiftasia.com/community/retrieval-augmented-generation-rag-a-comprehensive-guide-to-smarter-more-accurate-ai
질문 # 51
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Itcertkr 에서는 IT인증시험에 대비한 퍼펙트한Microsoft 인증AB-731덤프를 제공해드립니다. 시험공부할 시간이 충족하지 않은 분들은Itcertkr 에서 제공해드리는Microsoft 인증AB-731덤프로 시험준비를 하시면 자격증 취득이 쉬워집니다. 덤프를 구매하시면 일년무료 업데이트서비스도 받을수 있습니다.
AB-731테스트자료: https://www.itcertkr.com/AB-731_exam.html
참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Microsoft AB-731 시험 문제집이 있습니다: https://drive.google.com/open?id=1rSwBMW5-WbdvxYjfYeia4ximnLBv5P6U