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| Section | Weight | Objectives |
|---|---|---|
| Plan AI-powered business solutions | 25% | - AI strategy and requirements analysis
|
| Deploy AI-powered business solutions | 45% | - Testing and validation
|
| Design AI-powered business solutions | 30% | - Agent and solution design
|
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NEW QUESTION # 55
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: B
Explanation:
The scenario describes a deployed AI solution using Azure OpenAI that 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.
NEW QUESTION # 56
A company has a Microsoft Copilot Studio prompt-and-response agent.
You need to ensure that the agent meets the following requirements:
Provides effective and relevant responses
Provides conversational outcomes
Which metric should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Provides effective and relevant responses # Generated answer rate and quality Provides conversational outcomes # Topics by outcome Why "Generated answer rate and quality" is correct The requirement says the agent must provide effective and relevant responses. In Microsoft Copilot Studio, the metric that most directly evaluates whether the agent is successfully generating useful answers is Generated answer rate and quality.
This metric helps assess whether the prompt-and-response agent is:
returning answers consistently
producing responses that are useful
generating content of acceptable quality
handling user requests with enough relevance
From an AI business solutions perspective, response effectiveness is not just about whether the agent says something. It is about whether the generated output is meaningful, accurate enough for the scenario, and valuable to the user. That is exactly what generated answer rate and quality is designed to measure.
This metric is especially important in prompt-and-response solutions because these agents depend heavily on the quality of generated outputs rather than only predefined topic flows.
Why "Topics by outcome" is correct
The second requirement says the agent must provide conversational outcomes. The best metric for understanding whether conversations are reaching meaningful end states is Topics by outcome.
This metric helps evaluate what happens to conversations, such as whether they:
are resolved successfully
escalate
fail
abandon
complete a desired path
In enterprise AI and conversational business solutions, outcomes matter because stakeholders want to know whether the agent is actually driving the intended business result, not just generating text. A conversation can sound good but still fail operationally. Topics by outcome reveals whether the conversation reached a useful business conclusion.
For example, in a support or business-process scenario, leadership often wants to know:
how many conversations were resolved
how many required escalation
which flows underperform
where users get stuck
That is outcome measurement, and this metric aligns directly with that requirement.
Why the other metrics are not the best fit
Reactions
Reactions can provide feedback signals such as likes or dislikes, but they are not the strongest primary metric for determining whether responses are effective and relevant at a system level.
Satisfaction
Satisfaction is useful as a user sentiment metric, but it does not directly measure conversational outcomes. A user may be satisfied with tone but still not complete the intended business process.
Tool use
Tool use measures whether tools or actions are invoked, but it does not directly tell you whether responses are effective or whether conversations ended in successful outcomes.
NEW QUESTION # 57
You are designing a low-code Al business solution by using Microsoft Copilot Studio.
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?
Answer: C
NEW QUESTION # 58
A company has a customer order system that creates sales orders manually.
You need to design an AI solution to automate the following tasks as part of the system:
- Save the order details to a database.
- Update the order status in the database.
- Extract the order details from an order file.
- Prepare and send a confirmation email to customers.
The solution must minimize development effort and support intelligent automation and solution integration.
What should you include in the design?
Answer: A
Explanation:
To create this system, you'll need to integrate Microsoft Copilot Studio with Power Automate and AI Builder. This combination allows your agent to not only "talk" but also "act" by executing complex backend workflows.
Solution Architecture
Front-end: A Copilot Studio agent serves as the interface, receiving order requests or files from customers.
Brain (Logic): Power Automate cloud flows act as the "skills" for your agent, handling all database and email operations.
Data Extraction: AI Builder (Document Processing) extracts specific fields (like Item ID, Quantity, or Customer Name) from uploaded order files.
Storage: Microsoft Dataverse or SQL Server functions as the database to save and update order records.
Reference:
https://medium.com/@sushmita.sg/build-an-ai-powered-customer-support-system-in-2-hours-no- code-using-microsoft-copilot-2e3275dbbee5
NEW QUESTION # 59
A customer service agent deployed in Copilot Studio is receiving negative feedback from users.
The support team reports that the agent frequently provides incorrect answers about product warranty policies. The team wants to diagnose and resolve the issue.
Which monitoring approach should you recommend?
Answer: A
Explanation:
Effective agent monitoring starts with analysing conversation transcripts and topic analytics to understand exactly where the agent is failing. By reviewing which topics trigger when warranty questions are asked, you can determine whether the issue is incorrect topic routing, outdated knowledge source content, or poorly designed conversation flows. This diagnostic approach enables targeted fixes rather than wholesale rebuilding.
NEW QUESTION # 60
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