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| Section | Objectives |
|---|---|
| Embed agents and AI experiences | - Agent integration
|
| Build intelligent applications on Microsoft Power Platform | - Application development
|
| Implement automation and orchestration | - Process automation
|
| Governance and application lifecycle management | - Responsible AI and ALM
|
| Design intelligent applications with Copilot and natural language | - AI-first application design
|
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NEW QUESTION # 35
A company is designing an AI-enabled solution to help employees review and evaluate customer requests.
The company requires an AI summary to be generated at runtime by using instructions provided by the company, including tone, structure, and required data fields. The Prompt control will use these instructions on Power Apps forms across multiple tables.
You need to store the instructions.
Which component should you use?
Answer: C
Explanation:
To achieve a reusable, multi-table runtime AI summarization tool, you must use Custom AI Prompts configured via the AI Prompt Builder and stored in Microsoft Dataverse.
You should use AI Builder Prompt builder in the Microsoft Power Platform. It perfectly meets your requirements to create, store, and dynamically process instructions across multiple Power Apps tables and forms.
Prompt builder: Use this capability in the Power Apps Maker portal to draft, test, and save custom instructions for the AI. You can explicitly dictate the tone, structure, and required data fields within the prompt.
'Dynamic Values & Input Variables: Add dynamic input fields to your prompts so they ingest contextual data from different Dataverse tables and form controls.
Solution Lifecycle Management: The stored prompts are "solution-aware" components. They are saved directly in your environment and can be easily deployed and reused across different applications and tables.
Power Fx Integration: Trigger the AI summary at runtime by using Power Fx formulas to invoke your saved prompt. For example, on a button press, you would execute the formula:
Set(aiSummary, YourSavedPrompt.Predict(TextInput1.Text)).
Reference:
https://learn.microsoft.com/en-us/power-platform/release-plan/2025wave1/ai-builder/prompt-builder
NEW QUESTION # 36
A company is building a canvas app that spans multiple screens.
The app must store selected values across screens and hold tabular data during a session.
You need to manage application state.
Which two components should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
A global variable is appropriate for a selected scalar value that must remain available while the user moves between screens. It is created with Set and can be referenced anywhere in the canvas app for the remainder of the session unless changed. A collection is an in-memory table, so it is the correct component for holding multiple rows and columns of tabular data that controls such as galleries can consume. Context variables are scoped to a screen and would not reliably satisfy the cross-screen requirement. A data source is an external system or table rather than an application-state container; connecting to one does not itself preserve a temporary selection or session table. The maker should avoid duplicating authoritative data in a collection unless offline or transformation behavior requires it, and should clear sensitive session state when appropriate.
For large datasets, delegable source queries are preferable to indiscriminate collection loading. The Study Guide's exact canvas-app objective is "manage variables and collections." Options A and D match the required tabular and application-wide scalar state types.
Study Guide reference/topic: "Create canvas apps - Manage variables and collections." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 37
A developer needs an AI model to classify incoming emails into categories such as billing, technical support, and complaints. The categories are predefined and training examples are available. Which machine learning approach is appropriate?
Answer: D
Explanation:
Classification models assign data into predefined categories based on learned patterns. Email routing scenarios commonly use supervised classification because labeled examples exist.
Clustering is used when categories are unknown, regression predicts numerical values, and reinforcement learning focuses on decision-making through rewards rather than direct category assignment.
NEW QUESTION # 38
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result these questions will not appear on the review screen.
A company integrates external systems into Power Automate by using cloud flows.
The company must connect to external services with different characteristics and determine whether a custom connector is required.
You need to evaluate if a custom connector is necessary to meet the requirements.
Solution: A cloud flow must retrieve data from Microsoft Dataverse.
Does the solution meet the goal?
Answer: A
Explanation:
Microsoft Dataverse is already supported by the first-party Microsoft Dataverse connector, so a custom connector is not necessary. The connector includes actions such as List rows, Get a row by ID, Add a new row, Update a row, and Delete a row, as well as triggers for row events. For retrieval, the maker chooses Get a row for a known identifier or List rows for a query and can apply select, filter, order, expand, and pagination options where supported. The connection uses the platform's established authentication and Dataverse security model instead of a separately authored API definition. A custom connector would only be justified for an unsupported proprietary endpoint or functionality that the standard connector does not expose and that cannot be addressed through supported Dataverse operations. It would be wasteful to recreate common table access. The Study Guide's cloud-flow section includes "evaluate and recommend connectors" and "configure cloud flow actions." The standard Dataverse connector directly satisfies the requirement, so the proposed situation does not demonstrate a need for a custom connector. The correct response is No.
Study Guide reference/topic: "Create cloud flows - Evaluate and recommend connectors." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 39
A company creates an AI assistant that generates marketing content. Business users complain that responses vary significantly between identical requests. Which parameter should be adjusted?
Answer: A
Explanation:
Temperature controls randomness in generative AI output. Lower temperature values typically produce more consistent and predictable responses, while higher values increase creativity and variation. Storage, networking, and authentication settings do not influence model generation behavior. Adjusting temperature is a common method for controlling response consistency.
NEW QUESTION # 40
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