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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design AI-powered business solutions | 30% | - Agent and solution design
|
| Topic 2: Deploy AI-powered business solutions | 45% | - Security, governance, and ALM
|
| Topic 3: Plan AI-powered business solutions | 25% | - AI strategy and requirements analysis
|
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33. Frage
A company has a Microsoft Foundry project that uses a single agent and a single prompt to complete a series of tasks.
The agent encounters the following issues:
It frequently produces incomplete results.
It struggles with domain-specific reasoning.
Agent response times are remarkably slow.
You need to recommend a solution to improve the overall performance and accuracy of the agent.
What should you include in the recommendation? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all.
Antwort:
Begründung:
Explanation:
To improve performance # Move to a multi-agent architecture
To improve accuracy # Add a grounding data source
The current design uses a single agent and a single prompt to complete a series of tasks. That is often a bottleneck. When one agent is responsible for everything, it has to manage multiple steps, multiple reasoning modes, and multiple task transitions in one flow. This commonly leads to:
slower response times
task overload
incomplete outputs
reduced efficiency as complexity grows
Moving to a multi-agent architecture helps performance because tasks can be separated by function. For example:
one agent can handle task planning
another can retrieve domain knowledge
another can perform structured reasoning
another can prepare the final response
From an agentic AI systems perspective, decomposition improves throughput and execution quality. Instead of one overloaded agent trying to do everything, specialized agents handle narrower responsibilities. That often reduces latency in practical enterprise designs and improves the reliability of task completion.
This also addresses the symptom of incomplete results, because a multi-agent architecture can break a large workflow into smaller, controlled substeps.
Why "Add a grounding data source" improves accuracy
The agent struggles with domain-specific reasoning. That strongly suggests it lacks sufficient domain context during inference.
The best way to improve accuracy in this case is to add a grounding data source.
Grounding means giving the model access to trusted, relevant business knowledge at runtime, such as:
internal documentation
product specifications
policy manuals
knowledge bases
industry-specific reference data
This improves domain-specific reasoning because the model no longer relies only on general pretrained knowledge. Instead, it can anchor its responses in authoritative content.
From an AI business solutions standpoint, grounding is one of the most important mechanisms for improving:
factual relevance
domain accuracy
consistency
trustworthiness
explainability in enterprise contexts
When a model is inaccurate because it lacks business context, grounding is usually a better first fix than simply scaling model size.
Why the other actions are not the best fit
Add a prebuilt connector
A prebuilt connector helps with integration to systems and services, but it does not directly solve slow reasoning, incomplete output, or weak domain-specific reasoning unless the issue is specifically missing access to an external system. That is not the main problem described here.
Upgrade to a larger generative AI model
A larger model may sometimes improve reasoning quality, but it usually comes with higher cost and often slower response times, which works against the stated performance issue. It is not the best recommendation when the current agent is already slow.
Also, when domain-specific reasoning is the problem, grounding is usually more efficient and more controllable than simply choosing a larger model.
Expert reasoning shortcut
Use this exam logic:
Slow and overloaded single agent handling many tasks # move to multi-agent architecture Weak domain-specific reasoning # add grounding data source Need system integration # prebuilt connector Need raw generative capability increase, but can accept more cost/latency # larger model
34. Frage
A company has a Microsoft Copilot Studio agent that has been in production for three months.
The agent has received positive feedback from users.
You need to identify the number of questions unanswered by the agent and the number of abandoned sessions between the users and the agent.
Which Copilot Studio insights should you use? To answer, drag the appropriate insights to the correct requirements. Each insight may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
The number of unanswered questions # Generated answer rate and quality; The number of abandoned sessions # Conversation outcomes Why "Generated answer rate and quality" is correct The requirement is to identify the number of questions unanswered by the agent. In Copilot Studio, unanswered-question behavior is tied to how often the agent successfully generates answers and the quality of those answers.
The Generated answer rate and quality insight is the right place to evaluate whether the agent is:
answering user questions
failing to generate answers
producing low-quality responses
missing knowledge coverage
From an AI business solutions standpoint, unanswered questions are a direct signal of knowledge gaps, grounding gaps, or prompt-response weaknesses. This is exactly what generated answer analytics are meant to surface.
Why "Conversation outcomes" is correct
The requirement also asks for the number of abandoned sessions between users and the agent.
Abandonment is a conversation-level outcome, not a reaction or survey result. The Conversation outcomes insight tracks what happened to the interaction, such as whether the conversation was:
resolved
escalated
abandoned
otherwise completed unsuccessfully
That makes it the correct metric for identifying abandoned sessions.
Why the other insights are not correct
Reactions
Reactions capture signals like positive or negative user feedback, but they do not directly measure unanswered questions or abandoned sessions.
Survey results
Survey results reflect user satisfaction feedback after interactions, but they do not directly quantify unanswered questions or abandonment counts.
35. Frage
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
Your organization creates a new AI Center of Excellence (CoE) to guide enterprise-wide adoption of generative AI. A project team submits a proposal requesting immediate development of a generative AI model. They argue that identifying use cases and validating data quality can wait until after the prototype is built, since the CoE can "fix the data later." You are asked whether this approach aligns with Microsoft's recommended AI adoption lifecycle, which starts with identifying use cases, selecting domain-specific data, preparing and validating that data, designing and training solutions, and then monitoring and adapting them over time.
According to Microsoft's AI adoption guidance, is it appropriate to skip identifying use cases and validating domain-specific data before beginning AI model development?
Antwort: B
Begründung:
Microsoft's generative AI adoption framework - as shown in the diagram - emphasizes a sequenced lifecycle:
Identify use cases
Prepare, validate, and aggregate the required data
Design, train, and validate AI solutions
Monitor and adapt
The Microsoft Learn module clearly states that a Center of Excellence ensures organizations start with aligned business use cases and validated domain-specific data before any model development begins.
Skipping these early steps introduces high risk, creates misaligned solutions, and prevents effective contextualization of AI models.
Therefore, beginning model development without first identifying use cases and validating data does not follow Microsoft's recommended AI planning and adoption process.
References:
https://learn.microsoft.com/en-us/training/modules/intro-ai-center-excellence/2-how-center- excellence-assists-planning-adoption-generative-ai
https://learn.microsoft.com/en-us/training/modules/intro-ai-center-excellence/1-introduction- generative-ai-center-excellence
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/center-of- excellence
36. 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
37. Frage
Drag and Drop Question
You are designing end-to-end test scenarios for a business solution that uses Microsoft Dynamics
365 Sales and Dynamics 365 Finance.
You need to ensure that the business solution meets the following test requirements:
- Properly exchanges data between the Dynamics 365 apps
- Aligns with defined user workflows and business processes
Which type of testing should you use for each requirement? To answer, drag the appropriate testing types to the correct requirements. Each testing type 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.
Antwort:
Begründung:
38. Frage
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