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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan AI-powered business solutions | 25โ30% | - Analyze requirements for AI-powered business solutions
|
| Topic 2: Deploy AI-powered business solutions | 40โ45% | - Manage testing and validation
|
| Topic 3: Design AI-powered business solutions | 25โ30% | - Design AI and agents
|
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NEW QUESTION # 17
A company plans to implement an Al business solution for a consumer goods company.
You need to create agents that meet the following requirements:
* Orchestrate the sales order fulfillment and shipping of goods to customers.
* Analyze historical data and trends to replenish stock.
Which type of agent should you use for each requirement? To answer, drag the appropriate agent types to the correct requirements. Each agent 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.
Answer:
Explanation:
Explanation:
This question separates two different kinds of agent behavior.
For orchestrating sales order fulfillment and shipping , the best fit is an Autonomous agent. That requirement involves coordinating multiple steps, making decisions across a process, and driving execution across a workflow with limited manual intervention. Autonomous agents are designed for this kind of end-to- end orchestration.
For analyzing historical data and trends to replenish stock , the best fit is a Task agent. This requirement is more focused and bounded: analyze data, identify patterns, and support a specific business function. That aligns with a task-oriented agent rather than a broad orchestration agent.
Why Prompt-and-response is not the best answer here:
* It is better suited for direct user query/answer interactions
* It is not the strongest fit for process orchestration or structured business analysis workflows
NEW QUESTION # 18
You need to design a Microsoft 365 Copilot solution to optimize employee productivity. The solution must meet the following requirements:
- Ensure that the employees can query content stored in a subset of
Microsoft SharePoint Online sites and in Teams by using natural
language-based prompt actions.
- Ensure that employees receive contextually relevant responses in
Microsoft 365 Copilot.
What should you include in the design?
Answer: C
Explanation:
To enable Microsoft 365 Copilot to query a specific subset of SharePoint Online and Teams content using natural language, you can implement a combination of Restricted SharePoint Search and Microsoft Graph Connectors.
1. Restricting Content Access
You can limit the scope of data Copilot searches by using features that control which sites are indexed or accessible.
2. Configuring Microsoft Graph Access
Microsoft Graph acts as the bridge connecting Copilot to your organizational data. To integrate specific sources.
3. Enabling Prompt Actions & Context
Declarative Agents: You can create specialized Copilot Agents grounded in specific SharePoint knowledge sources. These agents use natural language instructions to focus on a subset of data for more contextually relevant responses.
Direct Referencing: Users can improve response relevance by explicitly naming files, folders, or Teams channels in their natural language prompts (e.g., "Summarize notes from the 'Product Launch' channel").
Reference:
https://nboldapp.com/advanced-microsoft-365-copilot-techniques-prompting-grounding-and- automation/
NEW QUESTION # 19
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.
Answer:
Explanation:
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
NEW QUESTION # 20
A company uses Microsoft Dynamics 365 Finance to manage accounts payable.
You are designing an AI invoice processing solution.
You need to recommend the prerequisites to configure a prebuilt copilot for accounts payable.
What should you recommend?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is D. From the Power Platform admin center, assign the Finance and Operations AI security role to users.
This question is asking for the prerequisite to configure a prebuilt copilot for accounts payable in Microsoft Dynamics 365 Finance. Since the copilot is already prebuilt, the requirement is not to create a new agent or build a custom AI tool. Instead, the needed prerequisite is proper access and security enablement for users.
Why D is correct
Prebuilt copilots in Dynamics 365 Finance and Operations apps rely on the platform's built-in configuration and security model. Before users can configure or use these AI capabilities, they must have the correct permissions. Assigning the Finance and Operations AI security role is the prerequisite that enables access to those AI experiences.
From a business solutions perspective, this makes sense because enterprise AI in finance functions must be governed carefully. Accounts payable touches:
invoices
vendors
payment workflows
financial controls
audit-sensitive business data
Because of that, Microsoft requires the appropriate security role before users can configure or interact with the prebuilt copilot capabilities.
This is also aligned with responsible deployment practice: enable access through role-based controls first, then configure and use the copilot.
Why the other options are incorrect
A). From Microsoft Copilot Studio, create an accounts payable agent
This is incorrect because the question specifically says prebuilt copilot. A prebuilt copilot does not require building a new custom agent in Copilot Studio as a prerequisite.
B). Extend Microsoft 365 Copilot for Sales to an accounts payable agent This is unrelated. Microsoft 365 Copilot for Sales is focused on sales workflows, not accounts payable in Dynamics 365 Finance.
C). Build an AI tool in Microsoft Foundry
This is also unnecessary for a prebuilt copilot scenario. Foundry is for custom AI solution development, not the prerequisite step for enabling an out-of-the-box accounts payable copilot.
Expert reasoning
Use this exam pattern:
If the question says prebuilt copilot, think enable/configure access, not build custom AI If the scenario is Dynamics 365 Finance / Finance and Operations, role-based setup is often the key prerequisite When the options include a specific AI security role, that is usually the required setup step So the correct choice is:
NEW QUESTION # 21
A company uses Microsoft 365 and Dynamics 365
You need to recommend a solution lo automatically summarize email threads, generate suggested replies in Microsoft Outlook and provide meeting preparation summaries that include relevant customer relationship management (CRM) data.
Solution: You recommend a classic Microsoft Dataverse workflow.
Does this meet the goal?
Answer: A
NEW QUESTION # 22
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