AB-731최고품질인증시험기출문제덤프공부자료

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참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Microsoft AB-731 시험 문제집이 있습니다: https://drive.google.com/open?id=1rSwBMW5-WbdvxYjfYeia4ximnLBv5P6U

Itcertkr는Itcertkr의Microsoft인증 AB-731덤프자료를 공부하면 한방에 시험패스하는것을 굳게 약속드립니다. Itcertkr의Microsoft인증 AB-731덤프로 공부하여 시험불합격받으면 바로 덤프비용전액 환불처리해드리는 서비스를 제공해드리기에 아무런 무담없는 시험준비공부를 할수 있습니다.

Itcertkr의 Microsoft AB-731덤프를 공부하면 100% Microsoft AB-731 시험패스를 보장해드립니다. 만약 Microsoft AB-731 덤프자료를 구매하여 공부한후 시험에 탈락할시 불합격성적표와 주문번호를 메일로 보내오시면 덤프비용을 바로 환불해드립니다. 저희 Itcertkr Microsoft AB-731덤프로 자격증부자되세요.

>> AB-731최고품질 인증시험 기출문제 <<

AB-731적중율 높은 시험덤프공부 & AB-731시험대비 덤프 최신문제

우리Itcertkr에는 아주 엘리트한 전문가들로 구성된 팀입니다. 우리는 아주 정확하게 또한 아주 신속히Microsoft AB-731관한 자료를 제공하며, 업데이트될경우 또한 아주 빠르게 뉴버전을 여러분한테 보내드립니다. Itcertkr는 관련업계에서도 우리만의 브랜드이미지를 지니고 있으며 많은 고객들의 찬사를 받았습니다. 현재Microsoft AB-731인증시험패스는 아주 어렵습니다, 하지만 Itcertkr의 자료로 충분히 시험 패스할 수 있습니다.

Microsoft AB-731 시험요강:

주제소개
주제 1
  • 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.
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 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.

최신 Agentic AI Business Solutions Architect AB-731 무료샘플문제 (Q14-Q19):

질문 # 14
Which business requirement most closely relates to grounding a generative AI model?

정답：D

설명：
Grounding in generative AI means ensuring model outputs are based on trusted, relevant information sources rather than only on the model's general training data. In a business context, grounding is about aligning responses with verified enterprise knowledge (policies, product documentation, internal procedures, approved FAQs, etc.) so the system is more accurate, consistent, and defensible. That is exactly what option D describes: "ensuring that verified company data sources are used for response generation." In Microsoft AI solution patterns, grounding is commonly achieved using retrieval-augmented generation (RAG). With RAG, the system retrieves relevant passages from approved company repositories (for example, indexed documents or knowledge bases) and supplies them as context to the model during response generation. This reduces hallucinations, improves factual correctness, and makes answers more relevant to the organization's reality-critical when AI is used for customer support, employee helpdesks, compliance guidance, or executive reporting.
The other options do not directly address grounding. A relates to localization/multilingual capability, B is a usage/telemetry metric, and C is an interaction method (natural language interface). They can all be important requirements, but none of them ensure outputs are anchored to verified company data-the core purpose of grounding.


질문 # 15
HOTSPOT - 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:
Answer Area
* Larger datasets can increase the cost of a generative AI solution that uses an Azure Machine Learning workspace. Answer: Yes
* The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed. Answer: Yes
* The cost of custom generative AI solutions always remains the same regardless of the model version or capability used. Answer: No
* Yes - In Azure Machine Learning, cost is driven by the resources you consume to store, process, and train. Larger datasets typically require more storage, more data transfer, and more compute time for preprocessing, training, evaluation, and experimentation. Even if you are not training foundation models, handling larger corpora can increase pipeline duration and the number/size of compute instances used, which increases overall cost.
* Yes - For Azure OpenAI usage under Standard (on-demand), pricing is primarily tied to token-based consumption (input tokens + output tokens). The more context you send and the longer the generated responses, the more tokens you consume, and the higher the cost. This is why prompt optimization, response length controls, and grounding strategies matter for cost management.
* No - Costs vary with model choice and capability. Different model families and versions have different price points, and larger/more capable models generally cost more per token or per unit of throughput. Additionally, architecture choices (RAG, vector search, caching), usage patterns, and throughput requirements can significantly change total cost-so it is never "always the same."


질문 # 16
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:
Answer Area
* Retrieval Augmented Generation RAG requires model fine-tuning. Answer: No
* Retrieval Augmented Generation RAG is helpful when you need a generative AI solution that can access current, verifiable information. Answer: Yes
* Retrieval Augmented Generation RAG enables you to get more relevant responses based on your organization's documents without retraining the base model. Answer: Yes RAG is an architecture pattern that improves generative AI responses by retrieving relevant information from external knowledge sources (for example, a document index, database, or knowledge base) and injecting that information into the model's prompt/context at runtime.
* No - RAG does not inherently require fine-tuning. Fine-tuning changes the model weights. RAG, instead, keeps the base model as-is and augments it with retrieved context. Fine-tuning can be complementary (for style, domain tone, or specialized tasks), but it is not required for RAG to work.
* Yes - RAG is especially valuable when you need current and verifiable information because the retrieval layer can pull the latest approved content (updated policies, product specs, incident runbooks) and provide it to the model. This reduces hallucinations and makes answers traceable to known sources.
* Yes - A major benefit of RAG is improved relevance to organizational documents without retraining. Instead of rebuilding the model whenever documents change, you update the underlying content store/index; the model then generates responses grounded in the retrieved passages, producing answers that align with your organization's latest information and terminology.


질문 # 17
Your company discovers that several employees use personal ChatGPT accounts to assist with work tasks.
You are concerned about proprietary data being shared externally. You need to evaluate the business value of rolling out Microsoft 365 Copilot. Which capability is a key benefit of using Copilot instead of a personal ChatGPT account?

정답：B

설명：
The core business concern in the scenario is data leakage -employees using consumer tools where corporate data could be pasted, stored, or processed outside the organization's governance boundary. The key differentiator of Microsoft 365 Copilot is that it's designed to work inside your Microsoft 365 tenant and to respect the organization's existing security, compliance, identity, and data access controls. Therefore, D is the best answer: Copilot accesses internal work data (Microsoft Graph-connected content such as mail, files, chats, meetings) in accordance with existing Microsoft 365 policies and permissions -meaning it can only surface content the user is already allowed to access, and it operates under enterprise-grade controls (authentication, auditing, compliance boundaries, and admin governance).
Options B and C describe general generative AI capabilities that personal ChatGPT can also provide (brainstorming, drafting, rewriting). A can be done in multiple tools as well, and it is not the primary
"enterprise value" difference tied to the stated risk. The scenario's driver is governance: reducing the likelihood of proprietary data leaving controlled systems while still enabling productivity. Rolling out Copilot addresses that by providing "work-safe" AI anchored to organizational content and managed through the same tenant controls your company already uses.


질문 # 18
Hotspot Question
Select the answer that correctly completes the sentence.

정답：

설명：

Explanation:
Box: crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is the process of ___________________.
Prompt engineering is the process of crafting, evaluating, and improving prompts to gain more accurate outputs from an AI model. Factors that improve prompts include the LLM's preferred format, specificity of language, appropriately identifying the audience's expectations, and making function calls for external data.
At its core, prompt engineering is about reducing ambiguity so the model doesn't have to "guess" what you want. It's the bridge between a vague idea and a high-quality output.
Beyond just clarity, modern prompting often involves specific frameworks like Chain-of-Thought (asking the AI to think step-by-step) or Few-Shot Prompting (providing examples) to significantly improve reasoning and accuracy.
Reference:
https://www.linkedin.com/pulse/using-prompt-engineering-optimize-genai-models-iabac-nfa9c


질문 # 19
......

Microsoft인증 AB-731시험을 패스하는 지름길은Itcertkr에서 연구제작한 Microsoft 인증AB-731시험대비 덤프를 마련하여 충분한 시험준비를 하는것입니다. 덤프는 Microsoft 인증AB-731시험의 모든 범위가 포함되어 있어 시험적중율이 높습니다. Microsoft 인증AB-731시험패는 바로 눈앞에 있습니다. 링크를 클릭하시고Itcertkr의Microsoft 인증AB-731시험대비 덤프를 장바구니에 담고 결제마친후 덤프를 받아 공부하는것입니다.

AB-731적중율 높은 시험덤프공부: 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

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