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| Section | Weight | Objectives |
|---|---|---|
| Design AI-powered business solutions | 30% | - Agent and solution design
|
| Deploy AI-powered business solutions | 45% | - Monitoring and optimization
|
| Plan AI-powered business solutions | 25% | - AI solution design strategy
|
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NEW QUESTION # 39
You need to design a shared prompt library that will be used across multiple business units. The solution must meet the following requirements:
Ensure consistent AI responses with reusable formats.
Support governance and version control.
Minimize administrative effort.
Minimize ongoing costs.
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.
Answer:
Explanation:
Explanation:
Ensure consistent AI responses # Define standardized prompt templates; Support governance and version control # Store prompts in a Git repository The requirement says the shared prompt library must ensure consistent AI responses with reusable formats.
The best way to achieve that is to create standardized prompt templates.
Standardized templates help multiple business units use the same structure for:
system instructions
response style
output formatting
safety guidance
business terminology
escalation behavior
This is important in AI-powered business solutions because prompt variability often leads to inconsistent outputs. A shared template approach creates repeatability and reduces drift across departments.
From an enterprise AI design perspective, standardized prompt templates improve:
response consistency
quality control
reusability
onboarding speed
compliance alignment
They also minimize administrative effort because teams do not need to invent new prompt structures from scratch.
Why "Store prompts in a Git repository" is correct
The requirement says the solution must support governance and version control while minimizing effort and cost.
A Git repository is the most appropriate answer because it provides:
version history
change tracking
rollback capability
collaboration support
approval workflows
auditability
This directly supports governance. It allows the organization to know:
who changed a prompt
what changed
when it changed
why it changed
That is extremely valuable for enterprise AI operations, especially when prompts influence business decisions, customer interactions, compliance behavior, or internal process automation.
Git also minimizes ongoing cost because it uses standard development and content management practices rather than requiring a separate expensive governance platform for prompt assets.
Why the other options are incorrect
Delegate department-specific prompt templates
This reduces consistency instead of improving it. Different departments creating their own templates introduces fragmentation, style drift, and governance challenges.
Maintain a prompt history
Keeping a history is useful, but it does not by itself ensure consistent reusable formats. It is more of a recordkeeping activity than a standardization mechanism.
Categorize prompts by business function
Categorization is helpful for organization and discoverability, but it does not provide version control or governance by itself.
Define standardized prompt templates for governance/version control
Standardization improves consistency, but version control specifically requires a system for tracking and managing changes. That is why Git is the better answer for the governance requirement.
Expert reasoning
A shared enterprise prompt library should usually follow this pattern:
Consistency and reuse # standard templates
Governance and versioning # Git-based repository
Low cost and low admin overhead # reuse existing tooling and centralized controls
NEW QUESTION # 40
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: C
Explanation:
Correct:
* Identify and address biased data.
To ensure that the customer feedback ingested by your Microsoft AI agent is clean and suitable for monthly sentiment reports, the most effective action is to identify and address biased data.Data bias in sentiment analysis can lead to skewed reports that misrepresent customer satisfaction or ignore the needs of specific demographic groups.
Incorrect:
* Create a workflow in Microsoft Power Automate
* Create an agent flow in Microsoft Copilot Studio.
* Ensure that the size of the database does not exceed 100 GB.
* Sort the database by customer last name.
Reference:
https://blog.naitive.cloud/how-to-detect-and-mitigate-bias-in-generative-ai/
NEW QUESTION # 41
A company has an Al solution named Solution1 that is deployed to the production environment. Solution!
uses an Azure OpenAI model to generate marketing emails for existing customers.
During an internal review, you identify that Solution1 creates different emails depending on the customers ' traits.
You need to recommend a strategy to mitigate the bias. The strategy must adhere to Microsoft responsible Al principles.
What should you recommend?
Answer: C
Explanation:
The scenario describes a deployed AI solution using Azure OpenAI th at exhibits bias (creating disparate outcomes based on customer traits). This directly impacts the Fairness principle of Microsoft ' s Responsible AI framework.
Why " Modify the system instructions " is the Correct Strategy:
* Direct Control via System Metaprompts: In large language model (LLM) applications like those powered by Azure OpenAI, the system instructions (or system message) define the behavior, constraints, and tone of the model. By modifying these instructions, you can explicitly direct the model to treat all customer segments equitably and ignore specific sensitive traits when drafting marketing content.
* Mitigation without Re-engineering: * Option B and D (Training/Retraining): Azure OpenAI models are foundation models. Most companies use th em via API and do not have access to the original " training dataset " to modify it. While fine-tuning is possible, it is significantly more expensive and complex than prompt engineering.
* Option C (Randomization): Randomization does not solve bias; it create s inconsistency and potentially irrelevant content, violating the Reliability and Safety principle.
* Alignment with Responsible AI: Microsoft ' s documentation on Fairness recommends " Instructional Mitigation. " This involves adding specific rules to the syste m prompt, such as: " You must ensure the tone and value proposition of the email remain consistent across all demographic groups " or " Do not use customer traits such as age or gender to influence the core marketing message. "
NEW QUESTION # 42
You need to recommend a Microsoft Power Platform solution for customer support. The solution must include Al capabilities in Microsoft Power Automate and must meet the following requirements:
* Use a centralized workspace for Al models.
* Generate short overviews from large amounts of unstructured text such as case notes or transcripts, without requiring additional training or coding.
What should you include in the recommendation for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
1. Centralized Workspace for AI Models
In the evolving Microsoft AI ecosystem, Azure AI Foundry (formerly Azure AI Studio) serves as the unified, centralized workspace for developers and organizations to build, manage, and deploy AI models.
Unified Hub: It provides a single location to access the Model Catalog, manage deployments, and perform prompt engineering.
Integration: While AI Builder is the " front-end " for citizen developers in Power Automate, Azure AI Foundry is the foundational " engine room " where the underlying models (including those used by prompts) are managed and governed at an enterprise level.
Scalability: It allows organizations to host their own custom models or utilize industry-leading foundation models (like GPT-4o) which then power the downstream capabilities in the Power Platform.
2. Generating Short Overviews Without Training or Coding
To summarize large amounts of unstructured text like case notes or transcripts without manual effort, AI Builder prebuilt prompts are the specific solution.
AI Builder Prebuilt Prompts: These are ready-to-use GPT-based instructions designed for common business tasks. Specifically, the " AISummarize " prebuilt prompt is engineered to take input text and generate a concise summary automatically.
Zero Training Required: Unlike " Custom Models " which require you to provide sets of historical data to teach the AI, prebuilt prompts leverage pre-trained Large Language Models (LLMs). This meets the requirement of " no additional training or coding. " Ease of Use: In Power Automate, you simply add the " Create text with GPT using a prompt " action and select the prebuilt summary prompt, passing it the text from your case notes or transcripts.
NEW QUESTION # 43
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 # 44
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