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| Section | Objectives |
|---|---|
| Topic 1: Implement automation and orchestration | - Process automation
|
| Topic 2: Build intelligent applications on Microsoft Power Platform | - Application development
|
| Topic 3: Governance and application lifecycle management | - Responsible AI and ALM
|
| Topic 4: Embed agents and AI experiences | - Agent integration
|
| Topic 5: Design intelligent applications with Copilot and natural language | - AI-first application design
|
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NEW QUESTION # 36
A company is developing an intelligent customer support application using Azure AI services. The solution must analyze customer conversations, identify user intent, and provide automated responses while maintaining conversation context across multiple interactions. The development team wants a managed service requiring minimal machine learning expertise. Which Azure capability should be implemented?
Answer: B
Explanation:
Azure AI Language conversational language understanding provides a managed natural language processing capability for identifying intents, entities, and user utterances. It is designed for conversational applications without requiring teams to build and train complex machine learning models. Azure Machine Learning and AKS are more suitable for custom ML scenarios requiring greater control over model development and deployment.
NEW QUESTION # 37
A company builds a canvas app for support agents to enter customer notes and generate a summarized response by using a custom prompt.
The company requires the following functionality built into the prompt:
- Generate the summary immediately when the agent selects a button in
the app.
- Supply current user-entered data to the prompt.
You need to execute the prompt.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
To fulfill your requirements, you should take the following two steps: Invoke the prompt directly from app logic and Pass user input as parameters to the prompt.
Invoke the prompt directly from app logic: Power Apps canvas apps allow you to call Dataverse custom prompts directly using the AIClassify, AISentiment, or AISummarize functions (or your specific custom prompt action) right from a button's OnSelect property. This fulfills the requirement to generate the summary immediately without the added latency or overhead of triggering a Power Automate cloud flow.
Pass user input as parameters to the prompt: To satisfy the requirement of using current user- entered data, you must map the text from your app's input fields (e.g., TextInput1.Text) directly into the input parameters defined within your custom prompt.
Reference:
https://learn.microsoft.com/en-us/ai-builder/use-a-custom-prompt-in-app
NEW QUESTION # 38
A company uses Dataverse to configure derived values for sales records.
The company requires the following functionality:
- Evaluate an expression by using numerical columns in the same row.
- Calculate the result dynamically when accessed.
- Ensure the result is NOT stored as part of the table data.
- The solution must minimize use of custom code.
You need to configure the app.
What should you do?
Answer: B
Explanation:
The best option to use is a calculated column (or its modern equivalent, a formula column).
Why?
Evaluates expressions in the same row: Calculated and formula columns natively execute calculations using other numerical columns belonging to the exact same row.
Calculates dynamically when accessed: Calculations are performed on-the-fly and evaluated in real-time when the data is read or displayed.
Not stored in table data: Because the value is dynamically generated at the database or query level whenever requested, it does not permanently occupy physical table data storage.
Minimizes custom code: These are out-of-the-box, low-code configurations inside Microsoft Dataverse utilizing simple Excel-like or Power Fx formulas.
Reference:
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/types-of-fields
NEW QUESTION # 39
You need to configure the generative AI reusability requirements.
What should you do?
Answer: A
Explanation:
Calling the prompt from a Power Automate cloud flow provides a reusable service-level entry point that can be invoked by different Power Platform experiences. The flow can accept parameters from each caller, pass those values into the prompt, and then route the generated result to an app, Dataverse, email, or another process. This addresses reuse more effectively than binding the prompt directly to one canvas app. An input variable is necessary for dynamic data, but by itself it does not establish cross-service orchestration; the flow exposes and manages those inputs for multiple callers. Returning JSON can standardize output handling but does not make the prompt reusable by itself. Direct canvas-app invocation is valid for an app-only requirement, not for the stated need to span Power Platform services. The flow should define explicit input types and validate required values so the case's inconsistent-input problem is not repeated. The AB-410 Study Guide lists both "add inputs to a prompt" and "consume a prompt in cloud flows." Option B provides the reusable execution layer while allowing dynamic values to be supplied at runtime.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume a prompt in cloud flows." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 40
A company builds a canvas app that processes images submitted by users and must classify them using an existing AI Builder model.
The app must generate predictions during user interaction.
You need to consume the AI model natively.
What should you do?
Answer: B
Explanation:
The existing AI Builder classification model should be invoked from the canvas app through Power Fx. The maker adds the model as an app data source or AI capability and calls its prediction operation from the relevant user action, passing the current image. The returned classification and confidence can then be stored in variables, displayed to the user, or written to Dataverse. A static formula cannot reproduce a trained image model's inference. Refining the model with additional images is a training-lifecycle task and may improve quality later, but it does not consume the existing model during the current interaction. Exporting the containing solution transports components between environments; it does not execute a prediction. The app formula should validate that an image is present, handle service errors, and define what happens when confidence is below the accepted threshold. The Study Guide contains the exact objective "consume an AI model in apps." Option A is the native runtime integration that classifies each user-submitted image during the active canvas-app session.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume an AI model in apps." AB-
410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 41
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