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問題 #70
A company has a Microsoft Foundry agent that summarizes customer feedback and recommends products to customers. The agent references data from multiple knowledge sources.
Users report that the agent response time is slow.
Telemetry data shows that the agent frequently reaches its token usage limit You need to recommend a solution to reduce token usage without degrading the quality of the generated responses.
What should you recommend?
答案：D
解題說明：
The problem is not just that the agent is slow. The telemetry specifically says it frequently reaches its token usage limit. That means too much content is being pulled into the prompt or context window before the model generates the answer.
The best recommendation is D. Reconfigure the prompts to limit the amount of retrieved content from the knowledge sources.
Why D is correct:
It directly targets the root cause: too many tokens from retrieved context It reduces unnecessary context while still keeping the most relevant information It helps preserve response quality better than simply cutting capabilities or hard-limiting output size Why the other options are less suitable:
A). Chunk documents during indexing can improve retrieval quality in some RAG scenarios, but it does not directly guarantee lower total retrieved token volume in the final prompt B). Lower the maximum token usage limit for the responses may reduce output length, but it does not solve excessive input-context usage and can hurt response quality C). Reduce the number of knowledge sources used by the agent is too blunt and may remove useful grounding unnecessarily
問題 #71
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.
答案：
解題說明：
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.
問題 #72
A company uses Microsoft Dynamics 365 Finance for accounts payable and customer debt recovery. You are designing an Al finance process that meets the following requirements:
* Provides Al-driven details to help staff identify overdue vendor invoices and outstanding balances
* Helps staff reduce how long it takes to review overdue invoices and payment history 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.
答案：
解題說明：
Explanation:
Vendor overdue invoices and outstanding balances # AI Summaries with Copilot Review overdue invoices and payment history faster # Collections coordinator summary This question combines two different finance-process needs inside Dynamics 365 Finance :
The first requirement is in the accounts payable / vendor context. Staff need AI-driven details to quickly understand overdue vendor invoices and outstanding balances. The best fit is AI Summaries with Copilot , because this feature is intended to surface concise, contextual summaries that help users understand finance records faster without manually opening and reviewing every transaction.
The second requirement is in the customer debt recovery / collections context. Staff need to reduce the time spent reviewing overdue invoices and payment history . The best match here is Collections coordinator summary , because it is specifically aligned to collections work and helps users quickly review the customer' s collections status, invoice aging, and payment-related context.
Why the other options are less suitable:
* Agent management is administrative and not the feature that provides invoice/balance insights.
* The Account Reconciliation agent is more focused on reconciliation tasks than on these specific AP and collections-review needs.
* The supplier Communication agent is oriented toward supplier interaction/communication, not primarily summarizing overdue invoice and balance insight for internal review.
* Analyze demand plans with Copilot is unrelated to overdue invoices or payment-history review.
問題 #73
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]
答案：B
解題說明：
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
問題 #74
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.
答案：
解題說明：
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
問題 #75
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