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NEW QUESTION # 63
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: C
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 # 64
You are designing two Microsoft Copilot Studio agents named Agent1 and Agent2. Each agent must meet the following requirements:
Each agent must use a standard model.
Each agent must NOT use generative orchestration.
Agent1 must support simple and short phrases for a given topic.
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.
You need to recommend language models for the agents.
What should you recommend for each agent?
Answer:
Explanation:
Explanation:
Agent 1 = NLU
Agent 2 = NLU and NLU+
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-overview Agent1 must support simple and short phrases for a given topic. That is the classic use case for NLU in Copilot Studio. NLU is designed for standard intent recognition where users enter brief, predictable utterances tied to a topic.
This makes NLU the best fit for:
narrow topic triggering
short phrase matching
standard, non-generative agent behavior
Why Agent2 = NLU+
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel. For that scenario, NLU+ is the correct recommendation among the listed standard models.
NLU+ extends the standard NLU approach and is the model aligned to scenarios that need stronger language understanding support in more advanced enterprise channel integrations such as voice experiences. Since the requirement explicitly says:
use a standard model
do not use generative orchestration
NLU+ fits better than Azure OpenAI or other generative options.
NEW QUESTION # 65
Case Study 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 technologies 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
Fabrikam'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 efficiency, 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 current 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 into 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 strategies 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 previous 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 sales 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:
- Azure 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 solution 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 checklist 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.
Hotspot Question
Which components should you use to meet the sales cycle enablement requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario:
Use low-code development to create a single AI agent that has Dataverse as its core component.
Box 1: Microsoft Copilot Studio
For AI agent creation
Scenario:
A designated checklist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution.
The Microsoft tool that should be used to create the AI agent is Microsoft Copilot Studio.
It is a low-code platform specifically designed for building and managing intelligent agents that integrate with Microsoft Dataverse as a core component for storing data and providing business context. To ensure the agent follows Microsoft's deployment recommendations, users should consult the implementation checklist provided within the Microsoft Copilot Studio documentation.
Key Features of Microsoft Copilot Studio:
Low-Code Interface: Uses a graphical builder or natural language to design agent behaviors and conversation flows.
Dataverse Integration: Leverages Dataverse as the enterprise data platform to ground agents in organizational knowledge and store shared context.
Governance and Compliance: Includes built-in responsible AI features and aligns with enterprise security standards.
Multi-Channel Deployment: Allows publishing agents across Microsoft Teams, websites, and mobile apps.
Box 2: a Fallback topic
For unexpected AI agent actions.
Scenario:
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.
In a low-code environment using Microsoft Copilot Studio with Dataverse, you can ensure unexpected AI actions lead to a live representative by configuring specific system topics.
1. Configure the System Fallback Topic
The Fallback system topic triggers when the AI agent cannot match a user's intent to any existing topic or knowledge source with sufficient confidence.
Enable Fallback: In Copilot Studio, navigate to Settings > General Settings > System fallback and click Add.
Action: Edit the Fallback topic to include a Go to topic node that redirects to the Escalate system topic.
2. Implement the Escalate System Topic
The Escalate topic is the primary mechanism for handoffs.
3. Handle Errors and Infinite Loops
Reference:
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/low-code-plugins-copilot- studio
NEW QUESTION # 66
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?
Answer: B
Explanation:
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
NEW QUESTION # 67
A company has an AI solution built by using Microsoft Copilot Studio and Power Platform. The solution is used by the company ' s sales, marketing, and customer service teams.
You are performing a return on AI investment (ROAI) analysis to evaluate the impact of the solution.
You need to identify which measurable business drivers to include in the analysis.
Which two business drivers should you identify? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answers are A. the reduced average case resolution time and D. increased employee productivity .
This question is asking for measurable business drivers for a ROAI analysis of a Copilot Studio and Power Platform solution used by operational teams.
For ROAI, the strongest business drivers are those that are:
* directly attributable to the AI solution
* operationally measurable
* tied to business outcomes
* relevant across teams
Why A is correct
Reduced average case resolution time is a strong measurable driver because it reflects a direct operational improvement in customer service and support workflows.
This metric can be quantified clearly by comparing:
* baseline resolution time before AI
* resolution time after deployment
That makes it ideal for ROAI because faster case resolution often leads to:
* lower service cost
* higher throughput
* better customer experience
* more efficient staffing
Why D is correct
Increased employee productivity is another core ROAI driver because AI solutions in sales, marketing, and customer service are often deployed specifically to reduce manual work and improve output per employee.
This can be measured through indicators such as:
* more tasks completed per agent or employee
* reduced manual effort
* increased throughput
* faster response cycles
* more time spent on higher-value work
From an AI business solutions perspective, productivity improvement is one of the most common and valid drivers in ROAI analysis.
Why the other options are incorrect
B). market capitalization
This is too broad and influenced by many external factors. It is not a practical direct business driver for evaluating the specific impact of one AI business solution.
C). economic market predictability
This is not a direct business driver created by the solution and is too external to the organization's operational AI ROI calculation.
E). brand awareness
Brand awareness can matter strategically, but it is less directly attributable and less operationally measurable than resolution time and productivity for this kind of internal business solution.
Expert reasoning
For ROAI questions, prefer metrics that are:
* operational
* attributable
* measurable before and after deployment
That leads to:
* reduced average case resolution time
* increased employee productivity
NEW QUESTION # 68
......
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