2026年GoShikenの最新AB-731 PDFダンプおよびAB-731試験エンジンの無料共有：https://drive.google.com/open?id=1yew28MbMYnfP-1wQmCJQXH_c9ECBoVYx
MicrosoftのAB-731認定試験の最新教育資料はGoShikenの専門チームが研究し続けてついに登場し、多くの人の夢が実現させることができます。今のIT業界の中で、自分の地位を固めたくて知識と情報技術を証明したいのもっとも良い方法がMicrosoftのAB-731認定試験でございます。がMicrosoftのAB-731「AI Transformation Leader」認定試験の合格書を取ったら仕事の上で大きな変化をもたらします。
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Format: | Case studies, Multiple choice, Yes/No, Drag and drop, Multiple response |
| Certificate Validity Period: | 12 months |
| Passing Score: | 700 |
| Real Exam Qty: | 40–60 |
| Exam Duration: | 45–65 |
| Exam Price: | $99 USD |
| Available Languages: | French, German, Japanese, Chinese (Simplified), English, Spanish |
| Recommended Training: | AB-731T00: AI Transformation Leader |
| Exam Registration: | Microsoft Certification Registration |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience in business transformation, change management, and familiarity with Microsoft 365 and Azure AI services |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
今日の職場では、さまざまなトレーニング資料とツールが常に混乱を招き、品質をテストするために余分な時間を費やしているため、学習に時間を浪費しています。実際、当社のAB-731テスト問題を完全に信じて、AB-731試験に合格することを100％保証します。また、AB-731テスト問題を購入してから1年間無料で更新できます。また、AB-731試験問題を購入する前に無料試用版を入手できます。 AB-731試験ダンプの利点は数え切れないほどあります。AB-731学習ガイドを購入するだけです！
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質問 # 30
- Select the answer that correctly completes the sentence.
An organization that runs continuous, large-scale workloads with Azure OpenAI models should choose the
__________ pricing model.
正解：
解説：
Explanation:
Provisioned (PTUs)
For continuous, large-scale workloads, the key needs are predictable throughput, consistent latency, and cost
/performance stability. Azure OpenAI Provisioned Throughput Units (PTUs) are designed for this scenario because you reserve model capacity to meet sustained demand. This reduces the risk of variability that can occur with purely on-demand usage during peak periods and provides a more predictable operating model when the workload is always "on" and high volume.
Standard (On-Demand) is best when usage is variable or you're starting small (PoCs, pilots, spiky workloads), because it is flexible pay-as-you-go but can be less predictable at sustained scale. Batch API is optimized for asynchronous, non-interactive processing where you can tolerate delayed results (for example, large offline summarization jobs), not for always-on, real-time interactions at scale.
Therefore, for continuous high-volume production workloads, Provisioned (PTUs) is the best pricing model choice.
質問 # 31
Your company manages a website that publishes daily news articles. You need to recommend an AI solution that can analyze text and identify the main people, locations, and companies mentioned in the articles. What should you include in the recommendation?
正解：B
解説：
The requirement is to analyze text and identify "people, locations, and companies" mentioned in news articles. This is a classic Named Entity Recognition (NER) / entity extraction scenario, which falls under natural language processing. Azure Language in Foundry Tools is the correct choice because it provides text analytics capabilities that detect and categorize entities in unstructured text-commonly including Person
, Location , and Organization . This enables downstream experiences such as topic tagging, search filters (e.
g., "all articles mentioning Company X"), trend dashboards (top people/places mentioned this week), and improved content discovery.
The other options do not match the requirement. Content Safety focuses on moderating harmful or policy- violating content (for example, hate, violence, self-harm, sexual content) and is not the primary tool for extracting named entities. Azure Vision is for analyzing images and performing OCR; it would only be relevant if the articles were images or scans, but the task here is entity extraction from text articles. Azure Speech is for speech-to-text, text-to-speech, and audio analysis; it would be used if your input were audio recordings rather than written articles.
Therefore, to identify key entities (people, locations, companies) from daily news article text, the best recommendation is Azure Language in Foundry Tools .
質問 # 32
- For each of the following statements, select Yes if the statement is true. Otherwise, select No.
正解：
解説：
Explanation:
The answer Area
* A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
The answer: No
* Content filtering and responsible AI safeguards help a generative AI model generate safe and inoffensive content. Answer: Yes
* A generative AI model always produces fair and unbiased results when the training data has been properly prepared and reviewed for fairness. Answer: No
* No - A larger training dataset can improve coverage and fluency, but it does not guarantee factual accuracy. Generative models can still hallucinate, mix concepts, or produce plausible-but-incorrect statements because they generate likely text rather than verifying truth. This is why solution designs commonly add grounding/retrieval, validation, and human review for high-stakes outputs.
* Yes - Content filtering and Responsible AI controls are specifically used to reduce harmful, unsafe, or policy-violating outputs . In practice, safeguards include input/output filters, safety classifiers, and governance controls that help enforce safety policies and minimize offensive content. These controls don't make outputs "perfect," but they materially reduce risk and are a standard part of production AI deployments.
* No - Even with careful data preparation and fairness reviews, models can still produce biased outcomes due to residual bias in data, label/measurement issues, deployment context, and shifting real- world distributions. "Always fair and unbiased" is an absolute claim that is not achievable in real systems; fairness is managed through continuous evaluation, monitoring, and mitigations-not assumed as guaranteed.
質問 # 33
Drag and Drop Question
Match the business scenario to the appropriate AI solution design approach.
To answer, drag the AI solution from the column on the left to its business scenario on the right.
Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
正解：
解説：
Explanation:
Box 1: Use Microsoft 365 Copilot
Summarizing emails (Outlook) and creating presentations (PowerPoint) are native, out-of-the-box productivity capabilities built directly into Microsoft 365 Copilot. There is no need to develop a custom solution or build novel connectors here; the standard user license fully covers these daily workplace workflows.
Box 2: Build with Microsoft Copilot Studio
Creating a customized, conversational agent (formerly Power Virtual Agents) to handle specific internal business logic-like answering HR policy questions and triggering a workflow to submit leave requests-falls squarely under Microsoft Copilot Studio. This allows you to build a tailored chatbot with specific conversation topics, triggers, and actions.
Box 3: Build with Azure Machine Learning
Predictive maintenance schedules require deep data science workflows, including custom data ingestion from manufacturing sensors (IoT), training custom regression or classification models, and processing time-series data. This requires the robust, full-lifecycle data science capabilities of Azure Machine Learning rather than an LLM-based productivity tool.
Box 4: Extend with Microsoft 365 Copilot connectors
The finance department wants to use their familiar productivity tools (like Excel or Teams via Microsoft 365 Copilot) but needs those tools to access outside, third-party data silos (ERP data).
To allow the native Copilot to securely read and interact with non-Microsoft 365 data repositories, you must Extend with Microsoft 365 Copilot connectors (via Graph connectors or Power Platform connectors).
質問 # 34
- 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:
Answer Area
* Azure Vision in Foundry Tools can extract and analyze key phrases from PDF files. Answer: No
* Azure Vision in Foundry Tools can generate images based on natural language descriptions. Answer:
No
* Azure Document Intelligence in Foundry Tools can be used to automate the processing of invoices and credit notes. Answer: Yes
* No - Azure Vision in Foundry Tools focuses on computer vision tasks such as image analysis and OCR (reading text from images and documents). While it can extract text from scanned PDFs via OCR, key phrase extraction is a natural language processing capability provided by Azure Language in Foundry Tools , not Azure Vision. Key phrase extraction analyzes text to identify main concepts, which is a different service family than vision.
* No - Azure Vision can analyze existing images (for example, generate captions/descriptions of an image), but generating new images from a text prompt is a generative model capability (for example, DALL E through Azure OpenAI/Azure AI Foundry model endpoints), not an Azure Vision feature.
Vision describes what it "sees"; it doesn't synthesize new images from natural language.
* Yes - Azure Document Intelligence in Foundry Tools is designed for intelligent document processing
, including automating extraction of structured fields from financial documents. Microsoft provides prebuilt models for invoices and supports custom extraction for similar document types, which makes it suitable for automating workflows involving invoices and credit-note style documents (field extraction, validation, routing).
質問 # 35
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AB-731模擬体験: https://www.goshiken.com/Microsoft/AB-731-mondaishu.html
ちなみに、GoShiken AB-731の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1yew28MbMYnfP-1wQmCJQXH_c9ECBoVYx