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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Exam Format: | Interactive items, Scenario-based, Multiple choice, Case studies, Multiple response |
| Real Exam Qty: | 40โ60 |
| Passing Score: | 700 (out of 1000) |
| Certificate Validity Period: | 1 year (annual renewal via free online assessment) |
| Available Languages: | English |
| Related Certifications: | Microsoft Certified: Power Platform Functional Consultant Associate Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Dynamics 365 Certified |
| Exam Duration: | 100 minutes |
| Exam Price: | $165 USD |
| Recommended Training: | Official Microsoft Learn learning paths |
| Exam Registration: | Register via Microsoft Learn / Pearson VUE |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Recommended: experience designing AI solutions, knowledge of Dynamics 365, Power Platform, Copilot Studio, Azure AI Foundry; no mandatory prerequisites |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-100 |
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NEW QUESTION # 95
A company has a Microsoft Copilot Studio agent that provides answers based on a knowledge base for customer support.
Users report that, occasionally, the agent provides inaccurate answers.
You need to use metrics from the Analytics tab in Copilot Studio to identify the cause of the inaccuracies.
Which two options should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
Explanation:
[C]
You can use the Analytics dashboard to identify and fix inaccurate responses by focusing on specific performance signals:
*-> Topics with Low Resolution: This metric identifies individual topics where sessions frequently end without a successful outcome. A low resolution rate often indicates that the topic's logic is flawed, the content is outdated, or it fails to address the user's specific intent.
*-> Topic Usage: High usage of certain topics combined with low resolution or high escalation rates points to critical knowledge gaps. Use this to prioritize which parts of your knowledge base need immediate refinement.
Unrecognized Utterances: Review these to find user phrases that didn't trigger any custom topic, indicating missing content or the need for new trigger phrases.
Analyze User Questions by Theme: Copilot Studio uses AI to group generative responses into themes. You can review these clusters to see which themes have poor response quality and need better grounding or data hygiene.
User Feedback (Thumbs Up/Down): Review specific messages with negative feedback in the Analytics tab to understand exactly where the AI is hallucinating or providing incomplete data.
[D]
In Microsoft Copilot Studio, you can use analytics to pinpoint why your agent is providing inaccurate answers by following a structured improvement checklist.
Using Metrics to Locate Problems
*-> Engagement Rate: A low engagement rate often indicates that your triggers are misconfigured or too broad, causing the agent to initiate the wrong topic or fail to recognize user intent entirely.
*-> Resolution Rate: Identify specific topics with low resolution. If a topic has a high volume of sessions but fails to reach a "Resolved" state, it usually means the knowledge source is outdated, mismatched, or the generative answers are not grounded properly.
*-> Escalation Rate: High escalation rates for particular topics are "red flag" drivers. Use the Escalation Rate Drivers chart to see which topics most frequently force a hand-off to a human agent, indicating where the AI's knowledge or logic is insufficient.
Reference:
https://learn.microsoft.com/en-au/microsoft-copilot-studio/guidance/analytics
https://support.accelevents.com/en/articles/5456691-session-analytics
NEW QUESTION # 96
A company has a Microsoft Power Platform environment that contains Microsoft Dataverse data.
You create a Microsoft Copilot Studio agent named Agent1 that processes the Dataverse data.
You discover that Agent1 fails to return relevant or accurate results. You need to improve the quality and reliability of data grounding. What should you do?
Answer: A
Explanation:
If a Copilot Studio agent that uses Dataverse is returning poor or irrelevant results, the most likely issue is weak or inconsistent grounding data.
The best corrective action is C. Verify and cleanse the Dataverse data.
Why C is correct:
Grounded AI quality depends heavily on source data quality
Incomplete, inconsistent, duplicated, outdated, or badly structured Dataverse records will lead to poor answers Cleansing and validating the data improves both relevance and reliability without requiring unnecessary model changes
NEW QUESTION # 97
A company has an AI agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent's output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
Answer: A
Explanation:
Correct:
* Identify and address biased data.
To ensure that the customer feedback ingested by your Microsoft AI agent is clean and suitable for monthly sentiment reports, the most effective action is to identify and address biased data.Data bias in sentiment analysis can lead to skewed reports that misrepresent customer satisfaction or ignore the needs of specific demographic groups.
Incorrect:
* Create a workflow in Microsoft Power Automate
* Create an agent flow in Microsoft Copilot Studio.
* Ensure that the size of the database does not exceed 100 GB.
* Sort the database by customer last name.
Reference:
https://blog.naitive.cloud/how-to-detect-and-mitigate-bias-in-generative-ai/
NEW QUESTION # 98
A company has a Microsoft Foundry generative AI model.
You need to evaluate the model's output to measure the overall quality and coherence of generated responses. The evaluation must use GPT-4o as a judge and return a numeric score for each output.
Which type of metric should you use?
Answer: A
Explanation:
To evaluate overall quality and coherence in Microsoft Foundry, use AI-assisted quality evaluators. These metrics use a "judge" model (like GPT-4o) to score responses on a numeric Likert scale (typically 1 to 5).
Recommended Metric: Coherence
The Coherence evaluator is the most direct fit for measuring how well-structured and logical a response is.
What it measures: The collective quality of all sentences and how they flow together.
Numeric Score: Returns an integer from 1 (Poor) to 5 (Excellent).
Output: Includes a coherence score, a coherence_reason (explanation from the judge), and a pass/fail result based on a threshold (default is 3).
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluate-generative-ai-app
NEW QUESTION # 99
A key stakeholder in your organization proposes immediately deploying Microsoft Copilot across all business units. They believe that AI tools will automatically generate superior, data-driven decisions from day one, regardless of the current quality of organizational data or the alignment of existing business processes.
Based on Microsoft's AI for Business guidance, should you agree that deploying AI tools like Copilot will automatically lead to better decisions even if the organization does not first establish high-quality business data and well-defined, aligned workflows? [Select Yes or No]
Answer: A
Explanation:
According to Microsoft's AI for Business guidance, AI tools like Microsoft Copilot do not automatically produce better decisions on day one if the organization lacks:
- High-quality, well-governed data
- Clearly defined and aligned business processes
- Proper data access, security, and governance
AI systems rely heavily on the quality, structure, and accessibility of underlying data. If the data is inconsistent, incomplete, or poorly managed, AI outputs can also be inaccurate or misleading.
Microsoft emphasizes establishing a strong data foundation and process alignment first before scaling AI across business units.
So it is not correct to assume AI alone will automatically generate superior decisions regardless of data quality or workflow readiness.
References:
https://www.microsoft.com/en-in/microsoft-copilot/copilot-101/ai-for-business#Customerexperience
https://www.microsoft.com/en-in/microsoft-365/business-insights-ideas/resources/grow-your-small-business-with-artificial-intelligence
NEW QUESTION # 100
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