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NEW QUESTION # 32
A company has a Microsoft Power Platform solution that contains the following components:
* Microsoft Dataverse tables
* A Microsoft Power Bl workspace named WS1
* A canvas app named App1 that uses Dataverse
* A Power Bl semantic model that connects to Dataverse by using DirectQuery You plan to use generative Al to provide answers to queries based on a subset of corporate data. You need to ensure that the data is available as a grounding data source for Al systems. What should you do?
Answer: A
Explanation:
The goal is to use generative AI to answer questions based on a subset of corporate data, and to ensure that this data is available as a grounding data source.
The solution includes:
Dataverse tables
a Power BI workspace
a canvas app
a Power BI semantic model using DirectQuery to Dataverse
The best action is D. Endorse the semantic model.
Why D is correct:
Endorsing a semantic model makes it a trusted, discoverable enterprise data asset It is the appropriate step when you want approved corporate data to be used reliably by downstream AI and analytics experiences It fits the requirement of exposing a curated subset of data as a grounding source rather than duplicating or manually exporting it Why the other options are not correct:
A). Export the semantic modelExporting does not make it a governed grounding source.
B). Share WS1Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding.
C). Populate a Dataverse tableThe data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.
NEW QUESTION # 33
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.
Answer:
Explanation:
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.
NEW QUESTION # 34
Which framework should you use to meet t he AI agent requirements for the sales cycle enablement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For Microsoft Copilot Studio best practices:
# Microsoft Power Platform Well #Architected Framework
Why?
* Copilot Studio is part of the Power Platform.
* Microsoft's official guidance for building scalable, s ecure, compliant Power Platform solutions is the Power Platform Well #Architected Framework .
* It includes governance, security, reliability, operational excellence, and cost optimization-exactly what Fabrikam needs for AI agent deployment, telemetry, compliance, and ROI.
For conversational user experiences:
# Success by Design
Why?
* Success by Design is Microsoft's methodology for implementing Dynamics 365 and Power Platform solutions.
* It includes conversational design checklists , solution blueprinting, risk assessments, and user #adoption strategies.
* Fabrikam's scenario emphasizes:
* User adoption
* Conversational UX quality
* Escalation paths
* Compliance
* Telemetry
* These are all covered in Success by Design's structured implementation approach.
https://learn.microsoft.com/en-ca/power-platform/well-architected/experience-optimization/conversation-design
Topic 1, Fabrikam, Inc
Background - Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technolog ies used by its business-to-business (B2B) sales team. As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations. Problem Statements - Fabrika m ' s infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes
, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience. Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month.
As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority. Planned Initiatives - General - Fabrikam management has prioritized AI-driven projects to improve efficie ncy, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration - Fabrikam plans to migrate from its cur rent on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users. All the data from the different systems will be consolidated int o a single data source - a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team. Sales Cycle Enablement - To achieve the company ' s objectives, Fabrikam intends to implement the following s trategies to enhance the sales cycle:
Use low-code development to create a single AI agent that has Dataverse as its core component. Ensure that sales managers can access unanswered correspondence from prospects and intervene as appropriate. Replace the pr evious proprietary software with Dynamics 365 Sales to track sales cycles and customer interactions.
Have the sales executives use Dynamics 365 Sales to track interactions for open opportunities and send follow-up communications to prospects. Have the sale s executives use handsfree headsets to interact with an AI agent when they have questions about internal policies or customer data. Requirements - Infrastructure Migration - Fabrikam has identified the following infrastructure migration requirements: Azu re must be used for all future infrastructure workloads. The company must follow Microsoft-recommended methodologies for infrastructure migration to the cloud. Any created AI agents must have their return on investment (ROI) calculated to ensure that the s olution will save the company money. Sales Cycle Enablement - Fabrikam has identified the following requirements for sales cycle enablement: The final AI agent must follow Microsoft recommendations for a conversational user experience. A designated checkl ist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution. Detailed telemetry must be logged for the first created AI agent to help troubleshoot and optimize the agent during the initial AI agent adoption process. Unexpected AI agent actions must end in an escalation to a live representative. For example, a sales executive must be rerouted to a representative if the agent cannot answer a question after two failed attempts. The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off. The sales team must use Dynamics 365 Sales to correspond with prospects more quickly and efficiently than currently. Sales managers must report on the adoption of the AI agent to key Fabrikam stakeholders on a monthly basis. Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
NEW QUESTION # 35
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: D
Explanation:
To automate feedback assessment and monthly reporting, you can build an agent flow in Microsoft Copilot Studio that functions as a "data pre-processor" and "insight generator." By using Agent Flows, you can move beyond simple Q&A to create structured, multi-step processes that clean raw data before it is ingested by the agent's main knowledge base.
Step 1: Design the Feedback Agent Flow
The "Flow" acts as the bridge between your cloud database (e.g., Azure SQL, Cosmos DB) and the final report.
Step 2: Ensure Data Quality (The "Clean Room")
For the agent to give accurate insights for marketing, the data must be grounded and structured.
Step 3: Automate the Monthly Report
Once the data is clean, the agent can summarize it into a report suitable for stakeholders.
Reference:
https://cps.co.uk/insights/how-to-use-copilot-studio-to-automate-repetitive-tasks
NEW QUESTION # 36
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?
Answer: D
Explanation:
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
NEW QUESTION # 37
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