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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | AI Transformation Leader |
| Exam Number: | AB-731 |
| Related Certifications: | Microsoft AI Business Professional (AB-730) |
| Available Languages: | English |
| Passing Score: | 700 |
| Exam Format: | Interactive components (may be included), Multiple-choice / scenario-based questions, Proctored online exam |
| Exam Duration: | 45 minutes |
| Exam Price: | $99 USD |
| Recommended Training: | AB-731T00: Drive AI transformation in your organization |
| Exam Registration: | Exam scheduling (Pearson VUE via Microsoft portal) Official Microsoft certification page |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored exam (may include interactive components) |
| Pre Condition: | No formal prerequisites required; designed for business leaders and decision-makers. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/ai-transformation-leader/ |
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| 主題 | 簡介 |
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| 主題 1 |
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| 主題 2 |
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| 主題 3 |
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問題 #95
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?
答案：A
解題說明：
Microsoft Foundry is an enterprise-grade platform specifically designed to help teams build, deploy, and manage generative AI solutions grounded in their own internal data.
While it is a powerful tool for this purpose, its target audience and complexity are important to distinguish:
*-> Building on Internal Data: The platform excels at this through Foundry IQ and Retrieval- Augmented Generation (RAG). It allows you to securely connect AI models to internal
"knowledge bases"-such as SharePoint, OneLake, or custom databases-so the AI provides responses based specifically on your company's context and data.
Target User: Contrary to being a tool solely for general business users, it is primarily an interoperable platform for developers, data scientists, and IT professionals. It provides deep technical tools like SDKs, CLI, and MLOps pipelines for scaling AI from a prototype to a full production application.
*-> Accessibility for Business Users: While its primary focus is developers, it does include low- code/no-code interfaces and visual "playgrounds". These allow non-technical contributors to experiment with models, test prompts, and participate in the development process without deep coding knowledge.
Reference:
https://www.softwebsolutions.com/resources/what-is-azure-ai-foundry
問題 #96
You need to recommend an AI solution for each task. The solutions must use prebuilt AI capabilities to reduce development time. What should you recommend for each task?
答案：
解題說明：
Explanation:
Extract information from invoices and credit notes: Azure Document Intelligence in Foundry Tools Identify discussion topics in customer service call recordings: Azure AI Search Detect customer sentiment and opinions: Azure AI Search To minimize development time, you should select services that provide prebuilt extraction and enrichment rather than building custom models.
For invoices and credit notes , the best fit is Azure Document Intelligence because it is purpose-built for intelligent document processing and includes prebuilt models (such as invoice-style extraction) that return structured fields (invoice number, vendor, totals, dates, line items). This directly satisfies "extract information" from financial documents without custom ML training.
For customer service call recordings , the raw input is audio, but the task is to identify "discussion topics," which is an information discovery and text-enrichment problem once the calls are transcribed. Azure AI Search is the correct recommendation because it can index large collections of content and apply built-in AI enrichment (for example, key phrase extraction and entity extraction) to help derive and query "topics" across many records.
For sentiment and opinions , Azure AI Search is again the best choice in this constrained list because it can run built-in enrichment skills over ingested text (such as transcripts or notes) to annotate content with sentiment-related metadata and then make that enriched data searchable and filterable at scale. This approach delivers fast time-to-value: ingest # enrich # index # query, without standing up a custom training pipeline.
問題 #97
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
The answer Area
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
The answer : Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
The 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.
問題 #98
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Microsoft Foundry is positioned as a unified platform experience for building, optimizing, and governing AI applications and agents. Microsoft explicitly emphasizes "fleetwide security and governance" and the ability to build and manage AI in a unified environment, which directly supports statement 1 being Yes : it is designed to help organizations build and operate generative AI solutions with centralized governance controls (for example, environment setup, data isolation, access control, and operational management).
For statement 2, Foundry supports scaling as demand increases . Microsoft documentation for Foundry- related model usage notes that as usage grows, Foundry can automatically increase quotas by moving users to higher tiers (and allows requesting additional quota). This is a concrete scalability mechanism tied to increased workload demand, so the statement is Yes .
For statement 3, Foundry is not limited to text-only generative AI. Microsoft provides "Azure Vision in Foundry Tools," which delivers computer vision capabilities such as analyzing images, reading text (OCR), and other image-processing features. That means Foundry can be used for image recognition/computer vision workloads, so the statement is Yes .
問題 #99
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.
答案：
解題說明：
Explanation:
Box 1: No
A text-to-image generator (like DALL-E or Midjourney) takes textual descriptions and synthesizes them into visual imagery. It does not perform language translation workflows; that task requires natural language processing (NLP) or large language models (LLMs).
Box 2: No
Predictive analytics models rely on historical data and machine learning algorithms to forecast future trends, behaviors, or numerical outcomes (such as click-through rates or future sales).
They do not create or synthesize brand-new creative artifacts like marketing text or ad copies; that is the role of Generative AI.
Box 3: Yes
Generative AI chatbots (like those built using custom LLMs or Copilot Studio) excel at maintaining dynamic, human-like context. They can handle highly personalized conversations based on user profile inputs and dynamically synthesize relevant product recommendations.
問題 #100
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