Create prompts and models in AI Hub
17 practice questions under this official exam objective (AB-410), each with the answer key, an option-by-option explanation and instant feedback.
Questions under this objective
17 total · page 1 of 1Which two actions implement Fabrikam's generative AI requirements?
This question tests how to choose the right AI Hub approach for a generative AI solution that minimizes custom development while supporting dynamic in
How to Create Custom Inspection Reports Natively in a Canvas App?
Fabrikam needs field technicians to generate custom inspection reports inside the new canvas app while minimizing custom AI code. This page explains w
Configuring Generative AI Reusability with Input Variables
Learn how to configure generative AI reusability in Microsoft Power Platform by using input variables to accept dynamic inputs, ensuring prompts adapt
Grounding an AI Hub prompt with Dataverse in Power Automate
This AB-410 prompt-building question tests which knowledge source supports Retrieval-Augmented Generation for a prompt used in Power Automate. The cor
How Should You Tune Prompt Model Settings to Reduce Unsupported Responses?
Prompt temperature controls how deterministic a model's output is when generating responses from internal support knowledge. Lowering the temperature
Reuse a Power Platform prompt across records with input parameters
To reuse one prompt across many customer feedback records without editing it each time, add input parameters in AI Hub. The correct answer is B, becau
Generate Complaint Responses by Running an AI Hub Prompt
Fabrikam must generate customer complaint replies from incoming email using reusable generative AI with dynamic inputs; the correct action is to run a
Consume an AI Builder model natively in a canvas app
This AB-410 question tests how a canvas app consumes an existing AI Builder model at runtime. The correct approach is A: invoke the model with Power F
Executing Custom Prompts in Canvas Apps
Learn how to execute custom AI prompts in Microsoft Power Platform canvas apps by invoking them directly via app logic and passing user input as param
Building Reusable Prompts in Copilot Studio
Learn how to build consistent and reusable prompts in Microsoft Copilot Studio using the Prompt Library templates for scalable customer service scenar
Defining Prompt Expectations for AI Summaries in Canvas Apps
Learn how to define the expectations portion of an AI prompt in a Canvas App to ensure accurate report summaries using business glossaries. Correct an
Configuring AI Prompts for Case Summaries in Power Apps
Learn how to use Prompt data types and field referencing in Microsoft Power Apps to automatically generate AI summaries of Dataverse records.
How to Natively Consume an AI Builder Model in a Canvas App
AI Builder models are consumed natively inside canvas apps by invoking the model from app logic (Power Fx / the model's prediction control) so predict
Power Platform AI Evaluation Components
Learn which Power Platform components capture input, evaluate it autonomously using AI, and route submissions based on business logic.
Consuming AI Builder Models in Canvas Apps
AI Builder models can be called directly inside a canvas app so predictions happen live during user interaction. This page explains why invoking the m
Copilot Studio Prompt Action: Input and Conversation Variables
In Microsoft Copilot Studio, a prompt action is configured with input variables that supply the customer name and case description, and with conversat
Consume AI Builder Models Natively in Canvas Apps with Power Fx
When a canvas app must call an existing AI Builder model during live user interaction, the native path is to reference the model as a Power Fx predict