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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Certificate Validity Period: | 1 year (for recertification) |
| Exam Format: | Multiple choice, Scenario-based |
| Exam Duration: | 50 minutes |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | English |
| Real Exam Qty: | Around 40-50 questions |
| Exam Price: | USD $85 |
| 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/ |
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NEW QUESTION # 70
You are creating validation criteria for a custom generative AI model that produces business reports based on internal enterprise data.
You need to assess whether the model's outputs are appropriate and meaningful for the business reports.
Which metric should you use?
Answer: D
Explanation:
To validate a custom generative AI model for business reports based on internal data, you should focus on alignment with domain-specific tasks through a mix of automated and human-centric metrics.
Validation Criteria for Business Reports
*-> Task-Specific Quality Evaluation (TSQE): This is your primary metric for assessing whether outputs are meaningful for specific business tasks.
* Groundedness and Factuality: Measure the model's ability to provide information strictly referenced from your internal enterprise data. This prevents "hallucinations" that could lead to poor business decisions.
* Domain-Specific Benchmarking: Compare AI outputs against "ground truth" data-verified, accurate reports previously created by human experts.
Reference:
https://www.prompts.ai/blog/how-to-evaluate-generative-ai-llm-outputs-with-structure-and- precision
NEW QUESTION # 71
Which tools should you recommend to assist the CISO and the CIO with their specific responsibilities? To answer, drag the appropriate tools to the correct executives. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
CISO # Azure Resource Graph Explorer
CIO # Microsoft Purview
Comprehensive and Detailed Explanation from Agentic AI Topics:
The CISO is responsible for discovering and inventorying AI resources for auditing across multiple Azure subscriptions. The best tool for that is Azure Resource Graph Explorer, because it is designed to query Azure resources at scale across subscriptions and provide visibility into what AI-related resources exist for governance and audit purposes.
The CIO is responsible for ensuring that appropriate security labels are assigned to the data used by the AI agents. The best tool for that is Microsoft Purview, because Purview supports data governance, classification, sensitivity labeling, and compliance management across enterprise data assets.
Why the other options are not correct:
Azure Blob Storage is storage, not the primary tool for discovery/inventory or labeling governance.
Copilot Studio is for building and managing agents, not for Azure-wide AI resource inventory or enterprise data labeling.
NEW QUESTION # 72
You are designing a mufti-agent solution to orchestrate a set of specialized agents.
You plan to include in agent that connects to a third party data source The agent must be discoverable and usable by users in Microsoft 365 Copilot and Microsoft Teams.
You need to recommend a development strategy for the agent
Which two tools should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection it worth one point.
Answer: C,D
Explanation:
Foundry Agent Service is appropriate for implementing and operating specialized enterprise agents, including agents that use external tools and data sources. It provides managed agent execution, identity, tools, versioning, publishing, and support for agent-to-agent scenarios, making it well suited to the specialized-agent component of a multi-agent architecture.
The Microsoft 365 Agents SDK provides the integration layer for code-first agents that need to operate across Microsoft 365 surfaces. Microsoft documents that Agents SDK-based solutions can integrate with AI services such as Microsoft Foundry and can be deployed to channels including Microsoft 365 Copilot and Microsoft Teams .
The Assistants API is not the preferred current agent-development architecture, and the Foundry SDK alone does not provide the Microsoft 365 multi-channel integration required by the scenario
NEW QUESTION # 73
You need to design an application lifecycle management (ALM) process for a Microsoft Power Platform environment that contains a solution named Solution1.
Solution1 must include a custom connector for Copilot in Microsoft Dynamics 365 Customer Service.
Solution1 must meet the following requirements:
* Ensure that the custom connector can be deployed consistently across environments as part of the ALM process.
* Allow the custom connector to be edited only in the development environment.
What should you include in the design?
Answer: A
Explanation:
The requirements are classic Power Platform ALM requirements:
the custom connector must be deployed consistently across environments
it should be editable only in development
The correct design choice is to add the custom connector to Solution1.
Why B is correct:
Putting the custom connector inside the solution makes it part of the ALM package It can then be exported and imported consistently across environments In production, when deployed properly through managed solutions, it is not freely edited there, which supports the requirement that editing happens only in development Why the other options are not correct:
A). Share the custom connector controls access, not ALM packaging and deployment consistency C). Create the custom connector in the default solution is not the recommended ALM approach for controlled deployment D). Add the custom connector to GitHub may help source control, but by itself it does not satisfy Power Platform deployment packaging across environments
NEW QUESTION # 74
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: B
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 # 75
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