참고: Itexamdump에서 Google Drive로 공유하는 무료 2026 Microsoft AB-100 시험 문제집이 있습니다: https://drive.google.com/open?id=1fW9mIOVndvfXl89daORcz1nii9EiiH7u
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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Real Exam Qty: | Around 40-50 questions |
| Passing Score: | 700 (out of 1000) |
| Certificate Validity Period: | 1 year (for recertification) |
| Exam Format: | Multiple choice, Scenario-based |
| Exam Price: | USD $85 |
| Available Languages: | English |
| Exam Duration: | 50 minutes |
| Related Certifications: | Microsoft Applied Skills |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | Basic understanding of Microsoft Copilot, Azure AI services, and general AI/ML concepts recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/applied-skills/agentic-ai-business-solutions-architect/ |
Itexamdump의 Microsoft AB-100덤프는 IT업계에 오랜 시간동안 종사한 전문가들의 끊임없는 노력과 지금까지의 노하우로 만들어낸Microsoft AB-100시험대비 알맞춤 자료입니다. Itexamdump의 Microsoft AB-100덤프만 공부하시면 여러분은 충분히 안전하게 Microsoft AB-100시험을 패스하실 수 있습니다. Itexamdump Microsoft AB-100덤프의 도움으로 여러분은 IT업계에서 또 한층 업그레이드 될것입니다
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질문 # 95
A company plans to deploy a Microsoft Copilot Studio agent that will analyze historical business data to predict customer behavior.
The data is currently stored in an Azure SQL database, flat files, APIs, and logs.
You need to organize the data into a format that can be used as a knowledge source in Copilot Studio.
What should you include in the solution?
정답：D
설명：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is A. Azure AI Search.
This scenario involves data coming from multiple sources:
Azure SQL database
flat files
APIs
logs
The requirement is to organize the data into a format that can be used as a knowledge source in Copilot Studio.
Why A is correct
Azure AI Search is the best answer because it is designed to ingest, index, and organize content from multiple heterogeneous data sources so that AI applications can retrieve and use relevant information effectively.
For Copilot and agent scenarios, Azure AI Search is especially useful because it supports:
unifying data from different sources
creating searchable indexes
enabling retrieval-based grounding
improving relevance for AI responses
From an AI business solutions perspective, when data is spread across structured and unstructured systems, Azure AI Search provides the retrieval layer that turns that fragmented data into a usable knowledge source.
It is much better suited than raw storage options because the question is not only about storing data. It is about organizing it for AI-driven access and use in Copilot Studio.
Why the other options are incorrect
B). Azure Data Lake Storage
Data Lake Storage is excellent for storing large volumes of raw and processed data, but by itself it does not provide the indexing and retrieval capabilities needed to make the content a strong knowledge source for Copilot Studio.
C). Azure Cosmos DB
Cosmos DB is a NoSQL operational database. It is not the primary service for consolidating and indexing multi-source business content into a knowledge source for Copilot Studio.
D). Azure Translator in Foundry Tools
Translator is for language translation, not for organizing business data into a knowledge source.
Expert reasoning
When the question asks how to make data from many sources usable as a knowledge source for an AI agent, think about the service that:
ingests
indexes
organizes
retrieves
That service is Azure AI Search.
질문 # 96
You are designing an AI strategy for Microsoft Dynamics 365 finance and operations apps. You are evaluating the use of Microsoft Copilot Studio to provide in-app help and guidance based on generative AI general knowledge.
You need to recommend which knowledge sources to include in the generative help and guidance agent. The solution must minimize the risk of generating inaccurate responses.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Custom knowledge sources # Must be uploaded to the agent; AI general knowledge # Must be disabled for the agent The requirement says the solution must minimize the risk of generating inaccurate responses . In generative AI business solutions, the best way to reduce hallucinations or loosely grounded answers is to rely on approved, scoped, domain-specific knowledge rather than broad general knowledge.
That means the agent should use:
* custom knowledge sources that are intentionally provided to the agent
* AI general knowledge disabled , so the agent does not answer from broad open-ended model knowledge when enterprise-specific guidance is needed In finance and operations scenarios, this is especially important because users may ask about:
* internal business processes
* financial controls
* operational procedures
* policy-specific guidance
* company-configured ERP workflows
These are areas where inaccurate answers can create operational or compliance risk. Grounding the agent on uploaded custom sources provides a more controlled knowledge base.
Why custom knowledge sources must be uploaded
If the goal is in-app help and guidance for Dynamics 365 finance and operations apps, the safest and most reliable approach is to provide the agent with curated documentation such as:
* internal process guides
* approved SOPs
* finance workflow documentation
* policy documents
* ERP-specific instructions
Uploading these custom knowledge sources ensures the agent answers from enterprise-approved material rather than from generic model knowledge.
From an AI governance perspective, this improves:
* factual grounding
* relevance
* auditability
* trustworthiness
* alignment to company-specific processes
Why AI general knowledge must be disabled
General AI knowledge can be useful in broad assistant scenarios, but in a finance and operations help context it increases the risk that the agent may generate:
* overly generic responses
* answers not aligned to company policy
* incorrect interpretations of internal process steps
* content that sounds plausible but is not operationally correct
Because the question explicitly says to minimize the risk of inaccurate responses , the safer design is to disable general knowledge and constrain responses to approved custom sources.
질문 # 97
You use Microsoft Copilot Studio analytics to analyze the performance of a deployed Copilot Studio agent.
You need to identify which performance metrics to use to measure the following:
* The percentage of engaged sessions that are escalated to a live customer service representative
* The number of agent queries that cause a knowledge source error
What should you identify for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
In Microsoft Copilot Studio analytics, each metric is designed to measure a different aspect of agent performance.
For the first requirement, the metric that tracks the percentage of engaged sessions escalated to a live customer service representative is Escalation rate. This directly measures how often conversations are handed off from the agent to a human.
For the second requirement, the metric that helps identify queries causing a knowledge source error is Answer quality. This area evaluates how well the agent responds and includes issues related to grounded answers, failed responses, and knowledge-source-related problems.
Why the other options are not correct:
Customer Satisfaction (CSAT) score measures user satisfaction, not escalation percentage or knowledge source errors.
Engagement rate measures whether users actively interact with the agent, not whether sessions are escalated or whether a knowledge source failed.
질문 # 98
You are designing two Microsoft Copilot Studio agents named Agent1 and Agent2. Each agent must meet the following requirements:
Each agent must use a standard model.
Each agent must NOT use generative orchestration.
Agent1 must support simple and short phrases for a given topic.
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.
You need to recommend language models for the agents.
What should you recommend for each agent?
정답：
설명：
Explanation:
Agent 1 = NLU
Agent 2 = NLU and NLU+
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-overview Agent1 must support simple and short phrases for a given topic . That is the classic use case for NLU in Copilot Studio. NLU is designed for standard intent recognition where users enter brief, predictable utterances tied to a topic.
This makes NLU the best fit for:
* narrow topic triggering
* short phrase matching
* standard, non-generative agent behavior
Why Agent2 = NLU+
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel . For that scenario, NLU+ is the correct recommendation among the listed standard models.
NLU+ extends the standard NLU approach and is the model aligned to scenarios that need stronger language understanding support in more advanced enterprise channel integrations such as voice experiences. Since the requirement explicitly says:
* use a standard model
* do not use generative orchestration
NLU+ fits better than Azure OpenAI or other generative options.
질문 # 99
You need to design a Microsoft 365 Copilot solution to optimize employee productivity. The solution must meet the following requirements:
- Ensure that the employees can query content stored in a subset of
Microsoft SharePoint Online sites and in Teams by using natural
language-based prompt actions.
- Ensure that employees receive contextually relevant responses in
Microsoft 365 Copilot.
What should you include in the design?
정답：A
설명：
To enable Microsoft 365 Copilot to query a specific subset of SharePoint Online and Teams content using natural language, you can implement a combination of Restricted SharePoint Search and Microsoft Graph Connectors.
1. Restricting Content Access
You can limit the scope of data Copilot searches by using features that control which sites are indexed or accessible.
2. Configuring Microsoft Graph Access
Microsoft Graph acts as the bridge connecting Copilot to your organizational data. To integrate specific sources.
3. Enabling Prompt Actions & Context
Declarative Agents: You can create specialized Copilot Agents grounded in specific SharePoint knowledge sources. These agents use natural language instructions to focus on a subset of data for more contextually relevant responses.
Direct Referencing: Users can improve response relevance by explicitly naming files, folders, or Teams channels in their natural language prompts (e.g., "Summarize notes from the 'Product Launch' channel").
Reference:
https://nboldapp.com/advanced-microsoft-365-copilot-techniques-prompting-grounding-and- automation/
질문 # 100
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