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| Section | Objectives |
|---|---|
| Topic 1: Embed agents and AI experiences | - Agent integration
|
| Topic 2: Implement automation and orchestration | - Process automation
|
| Topic 3: Design intelligent applications with Copilot and natural language | - AI-first application design
|
| Topic 4: Build intelligent applications on Microsoft Power Platform | - Application development
|
| Topic 5: Governance and application lifecycle management | - Responsible AI and ALM
|
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NEW QUESTION # 40
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: B
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 # 41
A company develops an AI chatbot using Azure OpenAI. Security administrators require that customer data must not be used to train public foundation models. Which statement correctly describes Azure OpenAI data handling?
Answer: D
Explanation:
Azure OpenAI provides enterprise data protection where customer prompts, completions, and training data are not used to train public OpenAI models. Data isolation and security controls are key enterprise features. Organizations can build applications while maintaining control over their information. Public model improvement processes are separate from Azure OpenAI customer workloads.
NEW QUESTION # 42
Case Study 2 - Fabrikam Inc
Background
Fabrikam Inc. is an energy provider that operates wind and solar farms across multiple countries and regions. The company uses Microsoft Power Platform to manage asset maintenance, technician workflows, and predictive analytics.
Field technicians use canvas apps on mobile devices to capture inspection and repair data.
Operations managers use model-driven apps to monitor asset performance and maintenance history.
The company plans to enhance its platform by introducing AI-driven insights, improving reuse across applications, and establishing a formal application lifecycle management (ALM) strategy.
Current environment
Applications
Field technicians use an outdated mobile app to log maintenance activities and capture asset readings in SharePoint Online.
Operations managers use a model-driven app to review maintenance records and asset performance trends.
Development practices
Developers build and modify applications directly in the production environment.
There is NO structured ALM process.
Solutions are NOT consistently used to package or deploy application components.
Reusable components are inconsistently implemented across apps, leading to duplication.
AI integration
Fabrikam Inc. plans to actively explore generative AI usage as part of the next phase of work.
Business requirement
Application development
The outdated mobile app must be replaced with a new app that supports responsive design principles, provides full control over the UI, and allows technicians to take and upload photos directly from their mobile devices.
The new app for the field technicians requires the name of the technician to be persisted on the home screen only. In addition, work orders must be displayed within a gallery.
All new apps must connect to a scalable, cloud-based relational data store.
Apps must support efficient data entry for technicians in the field.
Reusable UI elements must be implemented to reduce duplication.
AI capabilities
The company requires a generative AI solution to manage customer complaint responses and create custom inspection reports that align to an existing template.
Customer complaint responses must be generated in applications and based on emails being sent to a mailbox.
Custom AI development must be minimized.
Generative AI reusability
Generative AI solutions must accept dynamic inputs to adapt to different scenarios.
Generative AI solutions must be reusable across different Power Platform services.
Extensibility
Custom UI form components must be reusable across applications.
Use of server-side custom code must be minimized where possible.
Technical requirements
Application lifecycle management (ALM)
Separate environments must be used for development, testing, and production.
Solutions must be used to package application components, segmented based on functional areas.
Managed solutions must be deployed to the production environment.
Validation must occur before deployment to production.
Data processing
Aggregated values across related rows must be displayed for reporting.
Calculations must update automatically as new data is entered. Calculation data must be limited to a maximum length of 10.
Issues
Duplicate logic exists across multiple canvas apps.
Developers frequently overwrite changes in production.
Generative AI solutions produce inconsistent outputs due to missing or inconsistent inputs.
Maintenance metrics are not consistently calculated across related rows.
You need to provide a solution for field technicians to create custom inspection reports natively within the new app. What should you do?
Answer: B
Explanation:
Scenario: The company requires a generative AI solution to manage customer complaint responses and create custom inspection reports that align to an existing template.
You should use a custom prompt.
Generative AI Capability: Power Platform AI Builder prompts leverage large language models (like GPT) to natively handle generative text workflows.
Complaint Handling: A custom prompt can be built specifically to analyze customer feedback and automatically generate draft email or text responses.
Template Alignment: You can embed your exact inspection report layout and instructions directly inside the custom prompt's logic. This ensures that the generated outputs perfectly match the structure of your existing template.
Reference:
https://learn.microsoft.com/en-us/ai-builder/overview
NEW QUESTION # 43
Drag and Drop Question
A company uses a business process flow to manage case resolution.
The business process flow has the following requirements:
- Users must complete required data before progressing to the next
stage.
- Before exiting a stage, a background process must create an approval, assign it to the record owner, and send the approval by using Microsoft Teams.
- Mandatory data entry enforcement must be part of only the business
process flow itself.
You need to enforce the stage progression requirements.
How should you configure the business process flow? 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:
Box 1: Set the data step as required
To enforce mandatory data entry before a user can progress to the next stage of a Business Process Flow (BPF), you must use Data Steps marked as Required within the BPF designer itself.
Box 2: Add a Power Automate cloud flow
You should configure a Power Automate cloud flow triggered by a Dataverse row modification on the Business Process Flow (BPF) entity table, specifically filtering for when the Active Stage (_activestageid_value) changes.
Reference:
https://learn.microsoft.com/en-us/power-automate/business-process-flows-overview
NEW QUESTION # 44
You are designing a data model for a company. The model uses only standard Microsoft Dataverse tables.
You must extend the existing Accounts and Contacts tables to support the following business requirements:
# Store an additional classification value on the Accounts table.
# Add a field that indicates a customer's engagement tier on the Contacts table.
# Create a mechanism to improve lookup performance and ensure uniqueness across selected fields.
You need to modify the tables to support the business requirements.
Which components should you use? To answer, move the appropriate components to the correct requirements.
You may use each component 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:
Accounts classification - Choice column; Contacts engagement tier - Choice column; Lookup performance and uniqueness - Alternate key Both classification requirements should use Choice columns. An account classification and a contact engagement tier are controlled categorical values; Choice columns provide a governed option list, validation, labels, and consistent filtering. If the same option set is needed across tables, the maker should define a reusable global choice and reference it from both columns. A Customer column is a special polymorphic lookup that can reference an Account or Contact row; it represents a customer relationship, not a tier or classification. The lookup-performance and uniqueness requirement should use an alternate key. An alternate key creates a Dataverse key definition over selected columns so integrations can identify a row by business data and Dataverse can enforce uniqueness. The original embedded answer image is intentionally preserved, but the Customer-column selection for engagement tier is technically incorrect; the verified selection is Choice column. The Study Guide covers this under "create and modify columns" and "configure table properties." The final mapping is therefore Choice column for both categorical attributes and Alternate key for the lookup/uniqueness requirement.
Study Guide reference/topic: "Build data models - Create and modify columns; Configure table properties." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 45
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