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| Section | Objectives |
|---|---|
| Topic 1: Embed agents and AI experiences | - Agent integration
|
| Topic 2: Build intelligent applications on Microsoft Power Platform | - Application development
|
| Topic 3: Design intelligent applications with Copilot and natural language | - AI-first application design
|
| Topic 4: Governance and application lifecycle management | - Responsible AI and ALM
|
| Topic 5: Implement automation and orchestration | - Process automation
|
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NEW QUESTION # 28
A company builds a canvas app that users use to enter customer feedback. The company plans to use a preexisting custom prompt to summarize the feedback within the app The company must be able to:
* Use the prompt in the app.
* Access the result of the prompt by using Power Fx.
You need to add the prompt to the app and retrieve the results.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Add the prompt to the app: Data. Access the prompt results: Predict().
A preexisting custom prompt is added to a canvas app through the Data pane, in the same general way that a connector or AI model is added as an application data source. Adding it as media, a control, or a variable would not register the prompt as a callable app resource. After it is available in the app, Power Fx invokes it by using the prompt ' s Predict method and passes the current input values required by the prompt. The returned record contains the generated result, which can be assigned to a variable, collected, displayed in a control, or used in later formulas. The call should normally be placed in an event such as a button ' s OnSelect property so that it runs only when intended. Makers should also handle blank inputs and prediction errors and should avoid unnecessarily repeated calls because AI consumption is metered. Microsoft Learn identifies consuming prompts in apps and adding inputs to prompts as AB-410 objectives and documents calling a prompt from Power Apps through Power Fx.
Reference: https://learn.microsoft.com/en-us/ai-builder/use-a-custom-prompt-in-app Study Guide alignment: Build business application logic and automation.
NEW QUESTION # 29
You need to create the new app based on the requirements.
Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Technician name on one screen - Context variable; Work orders - Collection; Logic statements - Named formula
A context variable should hold the technician's name because the value is required only on the home screen.
Context variables are screen scoped, which limits the state to the place where it is needed and avoids creating wider application dependencies. A collection is the correct structure for work orders because it stores a table of records that a gallery can enumerate, filter, and display. A named formula is appropriate for reusable logic statements that should be calculated consistently without imperatively setting a variable. The original answer image selects a global variable for the technician name, but that scope is broader than required; the image is retained unchanged while the verified mapping uses Context variable. A global variable is appropriate when a scalar value must be available across screens, not when the requirement explicitly limits persistence to one screen. The app should also avoid loading an unnecessarily large work-order collection and should delegate filtering to the data source where possible. The Study Guide directly lists "manage variables and collections" and "named formulas" under Create canvas apps. The verified mapping applies each state mechanism at its intended scope and data shape.
Study Guide reference/topic: "Create canvas apps - Manage variables and collections; Create reusable components." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 30
A development team wants to improve an AI application's responses by providing specific instructions about tone, role, and expected behavior. Which prompt component should be used?
Answer: A
Explanation:
System messages define the behavior, role, and constraints of an AI assistant. They establish high-level instructions that guide model responses throughout conversations. Database indexes and storage resources manage information access but do not directly control model behavior.
Network routes only affect communication paths.
NEW QUESTION # 31
You need to configure a Power Automate flow to send the email with the results to the client. What should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Process Qualification records: Apply to each. Evaluate each Qualification: Condition.
List rows returns the Qualification records associated with the Service Request as an array. Apply to each is the correct control for processing every item in that fixed collection and building the Valid or Not Valid output required in the email. Inside the loop, a Condition evaluates the current Qualification ' s verification result or status and follows the appropriate branch to append the correct text. Do until is not the right selection for evaluating one record; it repeatedly executes actions until a termination expression becomes true and is intended for condition-driven polling or iterative work. The flow should initialize the email-body variable before the loop, append one formatted result per Qualification inside the appropriate condition branch, and send the message only after Apply to each completes. Microsoft's Power Automate guidance demonstrates Apply to each for an array and a nested Condition for testing a property of each current item.
Reference: https://learn.microsoft.com/en-us/power-automate/use-expressions-in-conditions Study Guide alignment: Build business application logic and automation.
NEW QUESTION # 32
A developer wants a generative AI application to consistently return responses in a structured format that another application can process automatically. Which technique should be applied?
Answer: C
Explanation:
Prompt engineering can define expected response formats, including JSON structures, fields, and formatting requirements. This improves interoperability between AI-generated responses and downstream applications. Increasing randomness reduces consistency, while removing instructions weakens model behavior control. Validation logic should complement AI output processing rather than being disabled.
NEW QUESTION # 33
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