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| Section | Weight | Objectives |
|---|---|---|
| Governance and Best Practices | 10-15% | - Implement data privacy and security controls - Ensure regulatory compliance - Apply responsible AI principles |
| Deploy and Monitor Agentic AI Solutions | 20-25% | - Configure monitoring and analytics - Optimize agent performance - Implement error handling and logging - Publish and deploy agent solutions |
| Implement Agentic AI Solutions | 30-35% | - Configure security and compliance settings - Implement data connections and plugins - Set up agent-to-agent communication - Configure AI agents using Microsoft Copilot Studio |
| Design Agentic AI Solutions | 30-35% | - Identify business scenarios for agentic AI - Select appropriate AI models and services - Define agent capabilities and boundaries - Design multi-agent architectures |
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18. Frage
A company has a canvas app named App1 in a Microsoft Power Platform environment named Env1.
Env1 uses a customer-managed key for data encryption. App1 connects to multiple data sources to retrieve and update customer and order information.
You need to recommend a solution to add Microsoft Copilot components to Appl. The solution must NOT modify the current security or encryption configurations of Env1.
What should you include in the recommendation?
Antwort: B
Begründung:
The deciding factor here is that Env1 uses a customer-managed key (CMK) for encryption, while the requirement is to add Microsoft Copilot components without changing the existing security or encryption setup of that environment.
In Power Platform, Copilot and generative AI capabilities have environment-level support requirements. A CMK-enabled environment can limit or prevent the use of certain Copilot capabilities . Because of that, the clean recommendation is to place the app in a different environment that supports Copilot features , specifically one using Microsoft-managed keys , instead of changing Env1.
Why D is correct:
* It allows Copilot components to be used in a compatible environment.
* It avoids changing the existing encryption/security configuration of Env1 .
* It satisfies the requirement that Env1's current configuration must remain untouched.
Why the other options are not correct:
* A. Duplicate App1 and republish the app in Env1 This keeps the app inside the same CMK-based environment, so it does not solve the compatibility issue.
* B. Enable Copilot features for Env1 If the environment's encryption model is the blocker, merely enabling features does not resolve that limitation.
* C. Modify the data sources of App1 to make them compatible with Copilot The issue is not mainly about the app's data sources. It is about the environment's support and encryption configuration.
19. Frage
A company deploys a Microsoft Copilot Studio agent that integrates with a Microsoft Power Automate desktop flow.
You need to recommend a testing solution that meets the following requirements:
Test cases must validate the most recent changes to the agent before the agent is released.
The flow must be validated as part of the agent ' s orchestration.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Validate most recent changes # Run tests against the latest unpublished version of the agent; Validate the flow as part of orchestration # Add the flow to the agent as a tool Why the first selection is correct The requirement says the company must validate the most recent changes to the agent before the agent is released. That means testing must happen on the newest working version that has not yet been published to production.
So the correct recommendation is:
Run tests against the latest unpublished version of the agent
This is the safest and most appropriate pre-release testing pattern because it allows the team to:
verify recent changes before users see them
catch regressions early
validate orchestration logic in a controlled state
reduce production risk
From an AI business solutions perspective, pre-release testing is critical for Copilot agents because even small changes in prompts, tools, orchestration, or data connections can affect:
response quality
workflow behavior
escalation paths
compliance behavior
user trust
Testing unpublished changes ensures that governance and quality assurance happen before release, not after deployment.
Why the second selection is correct
The question also requires that the Power Automate desktop flow be validated as part of the agent ' s orchestration.
That means the flow must participate directly in the agent's runtime action path. The correct way to do that is:
Add the flow to the agent as a tool
When a flow is added as a tool, the agent can invoke it during its orchestration. This allows test cases to validate not only the conversational layer, but also the actual execution of the flow inside the end-to-end agent behavior.
From an agentic AI design perspective, tools are how agents connect reasoning with action. If the flow is meant to be part of orchestration, it must be exposed to the agent in a way that allows the agent to call it during task execution.
That is the correct enterprise pattern for validating integrated behavior.
20. Frage
A company has an Al 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?
Antwort: B
Begründung:
The requirement is to make sure the data ingested by the agent is clean and suitable for the intended use , which is producing monthly sentiment insights to guide product development and marketing .
The best answer is C. Identify and address biased data.
Why C is correct:
* For sentiment analysis and reporting, biased data can distort conclusions and produce misleading recommendations
* Data preparation should include checking for skew, unfair representation, missing segments, and other quality issues that affect downstream decisions
* This aligns with responsible AI and sound analytics practice
Why the other options are not correct:
* A. Ensure that the size of the database does not exceed 100 GB is unrelated to data quality or suitability
* B. Translate the data into a single language might help in some implementations, but it is not universally required and is not the primary data-quality action here
* D. Sort the database by customer last name has no relevance to model readiness or report quality
21. Frage
A company has an Azure environment that supports multiple business units.
The company plans to implement an AI solution that will perform sentiment analysis on customer product reviews.
You need to evaluate the potential cost of the solution to support return on AI investment (ROAI) analysis.
What should you use?
Antwort: C
Begründung:
Correct:
* Azure pricing calculator (Best choice)
The best tool to evaluate the potential cost of a new AI solution to support ROI analysis is the Azure pricing calculator.
Why Azure Pricing Calculator is BestProactive Estimation: It is specifically designed to estimate the costs of Azure services before they are deployed.
Granular Planning: It allows you to select specific services (e.g., Azure AI Language, Cognitive Services, Azure Machine Learning), define usage patterns, regions, and tiers to create a detailed cost estimate.
Scenario Comparison: You can create different scenarios (e.g., comparing different language models or usage volumes) to estimate the impact on ROAI Comparison of Tools Azure Pricing Calculator: Estimates costs for new, un-deployed projects. (Best Choice).
Total Cost of Ownership (TCO) Calculator: Compares on-premises infrastructure costs against Azure, typically used for large-scale migration strategies, not specific AI service modeling.Cost Management + Billing: Monitors and analyzes actual, real-time spending on already deployed resources. For a complex AI solution involvin
* Total Cost of Ownership (TCO) Calculator (Second best choice)
While Cost Management + Billing is essential for tracking actual spend once a solution is running, the TCO Calculator is specifically designed for the pre-implementation phase to support Return on AI Investment (ROAI) analysis. It allows you to estimate and compare the potential costs of moving a workload to Azure-including labor, networking, and storage-against an existing process or alternative solution.
* Cost Management + Billing (Third best)
Azure pricing calculator and Total Cost of Ownership (TCO) Calculator are both better than Cost Management + Billing, as they evaluate the potential cost and support a Return on AI Investment (ROAI) analysis before the solution is built, you need to estimate future expenses based on expected usage. Cost Management + Billing does post-deployment tracking.
A good tool still for this scenario is Cost Management + Billing. Cost Management + Billing allows you to break down and evaluate costs across many business units using tags and scope filtering.
This granular data is essential for calculating the Return on AI Investment (ROAI) for specific departments or the entire organization.
Incorrect:
* Azure Monitor
* Azure Reservations
* Azure savings plans
Is used to reduce costs for compute services via long-term commitment. It is a way to lower your bill, but not a tool for evaluating or reporting on investment performance.
* Microsoft Fabric SKU Estimator
Is used to estimate capacity needs for Fabric-specific data projects. It is eseful only if your sentiment analysis solution is built specifically on the Microsoft Fabric platform.
Note:
* Azure pricing calculator
To evaluate the potential cost of an AI solution for sentiment analysis on Azure, you should use the Azure Pricing Calculator.This tool allows you to estimate the monthly and annual costs of the specific services required for your solution-such as Azure AI Language (which provides sentiment analysis) or Azure OpenAI Service-based on your expected volume of feedback data.
* Total Cost of Ownership (TCO) Calculator
To evaluate the cost of an Azure sentiment analysis solution and support a Return on AI Investment (ROAI) analysis for multiple business units, you should use the Azure Total Cost of Ownership (TCO) Calculator.
The TCO Calculator is essential for calculating the financial impact of migrating or implementing a new solution compared to your existing infrastructure.
Step-by-Step: Using the TCO Calculator for ROAI
To perform a comprehensive ROAI analysis, follow these steps to capture the full scope of ownership:
1. Define Your Current Workload
Enter details about your existing infrastructure that the AI solution might replace or augment.
2. Adjust Assumptions
Tailor the calculator to your business environment by adjusting key metrics.
3. Review the TCO Report
The calculator generates a detailed report comparing your current costs against Azure.
Reference:
https://www.synextra.co.uk/knowledge-base/azure-tco-vs-pricing-calculator
https://www.dev4side.com/en/blog/azure-ai
22. Frage
A company uses Microsoft Dynamics 365 Supply Chain Management.
You are designing an AI supply chain process that meets the following requirements:
Provides managers with AI-driven insights that surface key information from customer orders Helps planners use AI to anticipate future product needs more accurately You need to recommend which Microsoft Copilot features to include in the design.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Provide AI-driven insights from customer orders # AI Summaries with Copilot; Anticipate future product needs # Generative insights for Demand planning The first requirement is to give managers AI-driven insights that surface key information from customer orders.
That aligns best with AI Summaries with Copilot, because summaries are designed to extract and present the most important information from operational records in a concise, business-friendly way. In a supply chain context, this helps managers quickly understand:
important order details
exceptions or risks
priority items
fulfillment context
notable changes or issues tied to customer orders
From an AI business solutions perspective, this is exactly the kind of feature used to reduce manual review effort and improve decision speed. Rather than reading through many order records, managers get a synthesized view of key information.
Why "Generative insights for Demand planning" is correct
The second requirement is to help planners anticipate future product needs more accurately.
This directly maps to Generative insights for Demand planning. Demand planning is the business function focused on forecasting future demand, identifying trends, and improving planning accuracy for inventory and supply decisions.
Generative insights in this area help planners by surfacing patterns, explaining forecast behavior, and supporting better forward-looking decisions about product demand.
From an agentic AI business solutions standpoint, this is the right fit because it applies AI to:
forecast interpretation
trend identification
planning support
future demand anticipation
more accurate product need estimation
Why the other options are incorrect
Workload insights with Copilot
This is not the best match for surfacing key information from customer orders. It is more associated with operational workload visibility than customer-order summarization.
Microsoft Power BI
Power BI is useful for analytics and dashboards, but the question specifically asks for a Microsoft Copilot feature to anticipate future product needs. The direct feature match is Generative insights for Demand planning.
The Customer credit and collections workspace
This is focused on finance and collections activity, not on supply chain customer-order insight summarization.
Product information management
This manages product data and attributes, not AI-driven future demand anticipation.
The Supplier Communications Agent
This is related to supplier communication workflows, not demand forecasting for future product needs.
Expert reasoning
A quick exam shortcut here is:
Surface key information from records/orders # think AI Summaries with Copilot Anticipate future demand/product needs # think Generative insights for Demand planning
23. Frage
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