BONUS！！！ Pass4Test AB-731ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=11ewDQJwgSXaPiBpn5HIndGsLYfE3H1Nw
数年以来の整理と分析によって開発されたAB-731問題集は権威的で全面的です。AB-731問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からAB-731問題集への好評をもらいました。AB-731問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するAB-731問題集を暗記すれば、きっと試験に合格できます。
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
21世紀には、{Examcode}認定は受験者の特定の能力を表すため、社会でますます認知されるようになりました。ただし、{Examcode}認定を取得するには、AB-731試験の準備に多くの時間を費やす必要があります。合格しなくても、AB-731模擬試験の価格を支払う必要はありません。私たちがあなたに感銘を与えるのに十分な誠意を持っていることを望みます。
質問 # 10
An organization is exploring artificial intelligence solutions to automate content creation tasks such as drafting emails, generating marketing visuals, and producing software code suggestions.
Leadership wants to understand the core technology capability that enables these use cases.
Which of the following best describes generative AI?
正解：A
解説：
A technology that creates new content such as text, images, or code based on learned patterns is correct because generative AI systems learn from large datasets and produce original outputs such as written content, visuals, audio, video, or code using models like large language models and diffusion models.
Reference:
https://learn.microsoft.com/en-us/training/modules/understand-foundations-generative-ai- business-leaders/1-introduction
質問 # 11
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?
正解：D
解説：
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
質問 # 12
Your company uses a generative AI solution. You need to improve the quality of responses by using grounding. Which statement accurately describes how grounding improves accuracy and relevancy?
正解：C
解説：
Grounding is an AI solution pattern used to improve response quality by ensuring the model's output is based on trusted, relevant information provided at inference time, rather than relying only on what the model
"remembers" from training. Therefore, C is correct: grounding anchors responses in specific data sources .
In practical deployments, grounding commonly uses retrieval (often called Retrieval Augmented Generation, or RAG) where the system first finds relevant content from approved sources-such as internal policy documents, product documentation, knowledge bases, or databases-and then includes that content in the prompt context sent to the model. Microsoft's guidance describes grounding data as information supplied at inference time to help responses become more accurate and relevant because the model is guided by authoritative, up-to-date content that may not have been part of original training.
The other options do not define grounding. A relates to inclusion practices and diversity considerations, which are important for responsible AI but are not what grounding means. B describes transparency/explainability concepts. D relates to model evaluation/communication of limitations. Grounding is specifically about tying outputs to known sources, which reduces hallucinations and improves business trust in the generated responses.
質問 # 13
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.
正解：
解説：
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
質問 # 14
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
The correct answer is establish a champions program for your company. A champions program helps scale AI adoption because it creates a network of motivated employees who learn AI capabilities, share practical examples, support peers, and identify meaningful use cases within their departments. This is more effective than relying only on awareness campaigns or self-paced courses because champions create continuous peer-led adoption and practical reinforcement. Performance metrics are useful for measuring adoption, but metrics alone do not build confidence or capability. A champions program supports change management by turning early adopters into local advocates who can demonstrate value in real workflows, reduce resistance, gather feedback, and help employees understand how AI tools apply to their daily work.
質問 # 15
......
Microsoft資格試験はそんなに難しいのですか？弊社の資料を利用したら、AB-731試験は簡単になります。お客様に最高のMicrosoft問題集を入手させるために、我々は常に問題集の質を改善し、ずっと最新の試験のシラバスに応じて問題集を更新しています。我々のAB-731問題集の解答を暗記すれば、お客様は必ずこの試験に合格することができます。
AB-731合格問題: https://www.pass4test.jp/AB-731.html
BONUS！！！ Pass4Test AB-731ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=11ewDQJwgSXaPiBpn5HIndGsLYfE3H1Nw