참고: Itcertkr에서 Google Drive로 공유하는 무료 2026 Microsoft AB-731 시험 문제집이 있습니다: https://drive.google.com/open?id=1rSwBMW5-WbdvxYjfYeia4ximnLBv5P6U
Itcertkr에는 베터랑의전문가들로 이루어진 연구팀이 잇습니다, 그들은 it지식과 풍부한 경험으로 여러 가지 여러분이Microsoft인증AB-731시험을 패스할 수 있을 자료 등을 만들었습니다, Itcertkr 에서는 일년무료 업뎃을 제공하며, Itcertkr 의 덤프들은 모두 높은 정확도를 자랑합니다. Itcertkr 선택함으로 여러분이Microsoft인증AB-731시험에 대한 부담은 사라질 것입니다.
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Certified: AI Transformation Leader |
| Exam Number: | AB-731 |
| Certificate Validity Period: | 1 year (renewable) |
| Exam Price: | USD 99 |
| Available Languages: | English |
| Exam Format: | Drag and drop, Case studies, Build list, Multiple choice |
| Passing Score: | 700 / 1000 |
| Related Certifications: | Microsoft Certified: AI Transformation Leader |
| Exam Duration: | 45 minutes |
| Real Exam Qty: | 40-60 |
| 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 |
많은 사이트에서Microsoft 인증AB-731 인증시험대비자료를 제공하고 있습니다. 그중에서 Itcertkr를 선택한 분들은Microsoft 인증AB-731시험통과의 지름길에 오른것과 같습니다. Itcertkr는 시험에서 불합격성적표를 받으시면 덤프비용을 환불하는 서비스를 제공해드려 아무런 걱정없이 시험에 도전하도록 힘이 되어드립니다. Itcertkr덤프를 사용하여 시험에서 통과하신 분이 전해주신 희소식이 Itcertkr 덤프품질을 증명해드립니다.
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질문 # 34
Hotspot Question
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.
정답：
설명：
Explanation:
Box 1: Azure Document Intelligence in Foundry Tools
Extracting structured-data forms and invoices.
Azure Document Intelligence (formerly known as Azure Form Recognizer) within the Foundry Tools ecosystem can be used to extract structured data, including key-value pairs, tables, and specific fields from forms and invoices. It is designed to transform unstructured or semi-structured data from PDFs, images, and other files into actionable, structured JSON output.
Box 2: Azure Language in Foundry Tools
Summarizing written content from business reports.
Azure Language in Foundry Tools (formerly Azure AI Language) includes specific features for summarizing written content, including business reports, in both extractive and abstractive formats.
Key Summarization Capabilities
Native Document Summarization: This feature can directly parse and summarize files in their original formats, such as PDF, Word (DOCX), and plain text.
Summarization Approaches:
-Extractive: Selects the most important original sentences from the document to create a summary.
-Abstractive: Generates new, concise sentences that capture the main idea without directly copying the source text.
Powered by Advanced Models: The service utilizes Large Language Models (LLMs) and Small Language Models (SLMs), such as GPT-4o and Phi-3.5-mini, to provide high-quality, low-latency results.
Box 3: Azure Vision in Foundry Tools
Generate descriptive text for uploaded images.
Azure Vision in Foundry Tools (part of Azure AI Services within the Foundry ecosystem) can be used to analyze uploaded images and automatically generate descriptive, human-readable text.
This capability is part of the Image Analysis feature, which generates English-language captions describing the content of an image.
Key aspects of this functionality include:
*-> Descriptive Captions: The service generates complete sentences based on objects identified in the image, providing multiple options ordered by a confidence score.
*-> Image Tagging: It can generate a list of words (tags) identifying objects, beings, scenery, or actions.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/document-intelligence
https://azure.microsoft.com/en-us/products/ai-foundry/tools/vision
질문 # 35
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
* A generative AI solution is well-suited to predict next-quarter sales trends. Answer: No
* A generative AI solution can summarize lengthy policy documents. Answer: Yes
* A generative AI solution can create product descriptions from product specifications. Answer: Yes
* No - Predicting next-quarter sales trends is primarily a forecasting/predictive analytics problem.
Microsoft differentiates predictive AI (forecasting outcomes from historical patterns) from generative AI (creating content like text, images, or code). While you can use LLMs to assist analysts (explain trends, draft narratives), the core forecasting model is typically traditional ML/time-series methods rather than generative AI as the main engine.
* Yes - Summarization is a classic, high-value generative AI capability. Given a long policy, an LLM can compress it into executive summaries, key obligations, risks, and action items, often with formatting constraints (bullets, sections, "do/don't" lists). Microsoft highlights summarization and analysis as common generative AI use cases in business contexts.
* Yes - Generative AI is well-suited to transform structured inputs (features/specs) into natural- language outputs (product descriptions). This is straightforward "content generation," where you control tone, length, and required fields (benefits, differentiators, disclaimers). Microsoft also points to generating product descriptions and similar marketing/customer-facing text as a practical generative AI scenario.
질문 # 36
- 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
* Using incomplete or poor-quality data during generative AI model training can increase costs. Answer:
Yes
* AI models rely on training data to learn patterns and identify relationships to produce outputs. Answer:
Yes
* Generative AI models trained on non-representative datasets can produce inaccurate or unbalanced results. Answer: Yes
* Yes - Poor-quality or incomplete training data increases cost because it drives more iterations:
additional data cleaning, relabeling, re-training, and re-evaluation to reach acceptable performance. It can also increase operational costs after deployment if the model produces low-quality outputs that require human rework, escalations, or incident handling. In practice, data quality debt becomes model cost debt.
* Yes - Training data is the primary mechanism by which AI models learn statistical patterns and relationships. For generative models, the training corpus shapes language fluency, factual associations, style tendencies, and the kinds of content the model can produce. Without sufficient and appropriate training signals, outputs degrade.
* Yes - If the training dataset is not representative of the real-world population or business context, the model can systematically underperform for certain groups, topics, or edge cases. This can manifest as biased language, missing perspectives, and uneven accuracy, producing "unbalanced" results. That is why Responsible AI practice emphasizes representative data, evaluation across slices, and continuous monitoring.
질문 # 37
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
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line. Answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels. Answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
질문 # 38
Which business requirement most closely relates to grounding a generative AI model?
정답：C
질문 # 39
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AB-731유효한 덤프: https://www.itcertkr.com/AB-731_exam.html
참고: Itcertkr에서 Google Drive로 공유하는 무료, 최신 AB-731 시험 문제집이 있습니다: https://drive.google.com/open?id=1rSwBMW5-WbdvxYjfYeia4ximnLBv5P6U