Microsoftお客様との持続可能な関係に高い価値を置いているため、AB-731準備ガイドのヘルプの下で最高の証明書学習体験をお楽しみいただけます。まず、5〜10分でお支払いが完了すると、短納期で、オンラインでAB-731ガイドトレントをお送りします。加えて、当社のAB-731試験トレントの使用中に技術的および運用上の問題に対処するのに問題がある場合は、すぐにご連絡ください。24時間のオンラインサービスは、AI Transformation Leader問題をすぐに解決するための努力です。
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Certified: AI Transformation Leader |
| Exam Number: | AB-731 |
| Available Languages: | English |
| Real Exam Qty: | 40-60 |
| Certificate Validity Period: | 1 year (renewable) |
| Exam Format: | Drag and drop, Multiple choice, Build list, Case studies |
| Related Certifications: | Microsoft Certified: AI Transformation Leader |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 45 minutes |
| Exam Price: | USD 99 |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online or at a test centre |
| Pre Condition: | No formal prerequisites. Recommended: familiarity with Microsoft 365 services, Azure AI services, and experience with adoption or change management in a business context. This certification is designed for business decision-makers at all levels; no coding is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
人々は常に、特定の分野で有能で熟練していることを証明したいと考えています。能力を証明する方法はさまざまですが、最も直接的で便利な方法は、AB-731認定試験に参加し、認定証を取得することです。 AB-731認定に合格すると、非常に有能で優秀であることを証明できます。また、AB-731テストに合格することで有用な知識とスキルを習得できます。 AB-731ガイドトレントを購入すると、JPTestKingのAB-731試験に合格するのに役立ちます。時間と労力はほとんどかかりません。
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質問 # 44
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.
正解：
解説：
Explanation:
Box 1: Yes
Prompt engineering is the practice of designing, structuring, and refining text inputs given to a generative AI model. By providing explicit constraints, formatting parameters, and guidelines, it directly steers the model to yield highly accurate and relevant output.
Box 2: No
The part of the prompt that includes examples to demonstrate the desired output structure or style is called Examples (often referred to in context as few-shot examples or learning data). The Instruction is the specific task, command, or action you want the model to perform (e.g.,
"Summarize this document" or "Translate the text").
Box 3: Yes
The Context provides background data, domain-specific details, situational boundaries, or source documents (such as grounding data) that the model must reference or operate within to formulate a targeted and contextualized response.
質問 # 45
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?
正解：D
解説：
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
質問 # 46
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
質問 # 47
Your company plans to build a generative AI solution based on internal data. You recommend using Microsoft Foundry as a starting point to develop and manage the solution. What is a key benefit of using Microsoft Foundry for this project?
正解：D
解説：
Microsoft Foundry is positioned as a unified, enterprise-grade platform that helps organizations build, deploy, scale, and govern AI apps and agents-especially generative AI solutions that need to work with business context and internal data. That directly aligns with A : Foundry provides a scalable platform for developing and deploying generative AI solutions. Microsoft describes Foundry as an interoperable platform that makes it easier to build, deploy, and scale AI apps and agents, while also providing centralized security and governance features for organizations.
B is incorrect because Foundry does not remove model choice/configuration; in fact, it supports selecting among models and using tools/frameworks to build solutions. You still choose appropriate model(s), configure endpoints, and design grounding and safety controls.
C and D are not the best characterization of Foundry's primary benefit. While Foundry offers "friendly interfaces," Microsoft primarily positions it for developers, model builders, and enterprise AI operations
-not as a low-code platform for business users (that role is more commonly filled by Copilot Studio/Power Platform).
質問 # 48
- What should you use for each task? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
正解：
解説：
Explanation:
Answer Area
* Extracting structured data from forms and invoices: Answer: Azure Document Intelligence in Foundry Tools
* Summarizing written content from business reports: Answer: Azure Language in Foundry Tools
* Generating descriptive text for uploaded images: Answer: Azure Vision in Foundry Tools These three tasks align to three different Azure AI capability families: document processing, language understanding/generation, and computer vision.
* Forms and invoices are semi-structured documents where the business need is to extract specific fields (IDs, names, totals, dates) reliably into structured output. Azure Document Intelligence is designed for intelligent document processing and includes prebuilt models (such as invoices) as well as custom extraction options, making it the correct choice for structured data extraction from documents.
* Summarizing written business reports is an NLP task focused on compressing long text into key points, themes, and action items. Azure Language provides language processing capabilities (including summarization features within language service capabilities), so it is the best fit for summarization scenarios.
* Generating descriptive text for images (image captioning/description) is a computer vision task.
Azure Vision can analyze uploaded images and return descriptions/captions and other visual insights, which directly matches the requirement to produce descriptive text from images.
質問 # 49
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