当社TopexamのAB-731試験資料は、約98％〜100％の高い合格率と、高い合格率の両方を高めて、テストに合格するのがほとんど困難ではないことを示しています。 AB-731試験シミュレーションは、認定された専門家の勤勉な労働者からのリソースと実際の試験に基づいて編集され、過去数年の試験用紙を授与するため、非常に実用的です。 AB-731試験問題の質問と回答の内容は洗練されており、最も重要な情報に焦点を当てています。クライアントが実際のAB-731試験の雰囲気とペースに慣れるために、試験を刺激する機能を提供します。
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近年、IT技術の急速な発展に伴って、IT技術を勉強し始める人がますます多くなっています。そこで、IT業界で働く人も多くなっています。このように、IT業界の競争が一層激しくなります。同様にIT業界で働いていて、IT夢を持っているあなたは、きっと他の人にキャッチアップされ、追い抜かれることを望まないでしょう。それでは、ずっと自分自身のスキルをアップグレードすることが必要になり、他の人に自分の強さを証明する必要があります。では、どうやって自分の能力を証明するのですか。多くの人々はIT認定試験を受験して認証資格を取ることを通して彼らの強さを証明します。あなたもIT認証資格を取りたいですか。まずMicrosoftのAB-731認定試験に合格しましょう。これはMicrosoftの最も重要な試験の一つで、業界全体に認証された資格です。
質問 # 56
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant. The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate. What is a possible cause of the issue?
正解：B
解説：
A common reason models degrade after being successful in production is data drift (also called concept drift).
Over time, the distribution of input data changes-for example, customer behavior shifts, product catalog changes, seasonality changes, new categories appear, sensors get recalibrated, or business processes evolve.
When the model sees data that differs from what it was trained on, its predictions can become less accurate.
This is exactly what option A describes and is the most likely "six months later" cause.
Option B is not a primary explanation for reduced predictive accuracy. More compute can improve throughput
/latency, but it does not inherently improve correctness of predictions. If anything, compute constraints typically cause timeouts or slower responses, not a systematic accuracy drop.
Option C (trained incorrectly) would usually manifest earlier-poor performance from the start-unless the
"incorrectness" is that the model was trained on a snapshot that later became stale (which again maps back to drift). The correct operational response is to monitor for drift, validate performance regularly, and retrain
/refresh the model using newer representative data and updated features/labels.
質問 # 57
Select the answer that correctly completes the sentence.
Prompt engineering is the process of __________.
正解：
解説：
Explanation:
crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is fundamentally about how you communicate intent to a generative AI model so it produces outputs that meet business expectations. The best completion is "crafting clear instructions to guide generative AI solutions in generating context-appropriate content" because it captures the practical, day-to- day discipline: shaping the input (prompt) with the right task framing, constraints, context, and output format.
In real deployments, prompt engineering includes specifying the role and objective (for example, "act as a customer support agent"), providing the necessary context (product details, policy excerpts, audience), adding explicit requirements (tone, length, must/must-not statements), and defining structured output (JSON fields, bullet sections, headings). It can also include adding examples (few-shot prompting), clarifying what to do when information is missing, and instructing the model to cite only provided sources or to ask follow-up questions. These techniques reduce ambiguity, improve consistency, and lower the risk of hallucinations or off-brand responses.
The other options are not accurate definitions. "Integrating AI-powered tools into business workflows" describes solution adoption/integration, not prompt engineering. "Identifying and fixing errors in AI- generated content" is review/editing or quality assurance. "Designing, developing, and training generative AI models" is model development/ML engineering. Prompt engineering operates without changing model weights ; it's about steering model behavior through well-constructed instructions and context.
質問 # 58
You need to recommend a service that supports indexing information and knowledge mining by extracting insights from documents. What should you recommend?
正解：B
解説：
The requirement has two key phrases: indexing information and knowledge mining by extracting insights from documents . The Microsoft service purpose-built for this is Azure AI Search (formerly Azure Cognitive Search), which provides a search index over your content and supports "AI enrichment" workflows to extract and structure insights from documents during indexing.
Azure AI Search can ingest content from common enterprise sources (files, blobs, databases), build searchable indexes, and enrich the indexed content using built-in skills or integrated AI capabilities-such as entity recognition, key phrase extraction, language detection, and OCR (depending on the pipeline). This is exactly what "knowledge mining" refers to: turning large volumes of unstructured documents into structured, searchable knowledge that applications and users can query.
The other choices are partial fits: Azure Vision focuses on image/video analysis, not general document indexing. Azure Document Intelligence is excellent for extracting fields/tables from forms and documents, but on its own it does not provide the full indexing/search and knowledge mining layer across a corpus.
Microsoft Foundry is an overarching platform for building AI apps/agents; it can incorporate search, but the specific service that directly delivers indexing + knowledge mining is Azure AI Search .
質問 # 59
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Box: uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot _______________.
The Analyst agent in Microsoft 365 Copilot is a specialized, AI-powered "virtual data scientist" designed to transform raw, structured data into actionable insights, utilizing text, tables, charts, and graphs to present findings.
It is designed to work with structured data sources, including Excel files, CSVs, tables, and databases, to help users analyze data without needing advanced data science expertise.
Reference:
https://blog.storyals.com/sv/meet-your-new-ai-teammates-researcher-analyst
質問 # 60
Match the business scenario to the appropriate AI solution design approach. Each solution may be used once, more than once, or not at all.
正解：
解説：
Explanation:
* The marketing department at your company wants AI to summarize emails and create presentations. Answer: Use Microsoft 365 Copilot
* The HR department at your company wants a conversational agent for policy questions and leave requests. Answer: Build with Microsoft Copilot Studio
* The manufacturing department at your company wants AI to predict maintenance schedules. Answer: Build with Azure Machine Learning
* The finance department at your company wants AI-powered access to enterprise resource planning ERP data by using familiar productivity tools. Answer: Extend with Microsoft 365 Copilot connectors These scenarios map to four distinct solution patterns: out-of-the-box productivity assistance, low-code conversational agents, predictive ML, and enterprise data integration.
Marketing's need to summarize emails and create presentations is a core "productivity copilot" use case.
Microsoft 365 Copilot is embedded in Outlook, Word, PowerPoint, and Teams, so it directly supports summarization, drafting, and presentation generation without building a custom solution-making Use Microsoft 365 Copilot the best fit.
HR's requirement is a conversational agent tailored to internal policies and workflows such as leave requests.
That typically needs custom dialog, grounded knowledge sources, and possibly actions/workflows. Microsoft Copilot Studio is designed to build and manage such agents with organizational knowledge and business process integration, so Build with Microsoft Copilot Studio fits best.
Manufacturing's predictive maintenance scheduling is classic predictive analytics: learning patterns from historical telemetry/maintenance data to forecast failures or optimal service windows. This is best addressed with Azure Machine Learning , which supports training, evaluating, and deploying custom predictive models.
Finance wants AI-powered access to ERP data "using familiar productivity tools," which implies bringing external line-of-business data into the Microsoft 365 Copilot experience. That is precisely where Microsoft
365 Copilot connectors help-indexing and exposing enterprise data sources so Copilot can reference them in a governed way-so Extend with Microsoft 365 Copilot connectors is the best approach.
質問 # 61
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