7.3 Model-Driven App Charts and Dashboards

Microsoft Certified: Intelligent Applications Builder Associate AB-410

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1 Introduction

1.1 About the AB-410 Exam 1.2 How to Use This Study Guide

2 Chapter 2: Designing Power Platform Solutions with AI-Enabled Tools

2.1 Analyzing Requirements and Identifying Components 2.2 Evaluating Built-in Agents for Business Solutions 2.3 Recommending Extensibility Options

3 Chapter 3: Environments and ALM Strategy

3.1 Recommending Environment Types 3.2 Solution and Application Lifecycle Management Strategy

4 Chapter 4: Dataverse Tables and Columns

4.1 Creating and Editing Tables in the Data Workspace 4.2 Configuring Table Properties 4.3 Creating and Modifying Columns

5 Chapter 5: Relationships, AI Data Features, and Data Security

5.1 Configuring Table Relationships 5.2 Configuring Prompt Columns and Row Summaries 5.3 Configuring Public Views and Main Forms 5.4 Configuring Dataverse Security

6 Chapter 6: Model-Driven App Forms and Views

6.1 Creating and Configuring Forms for Model-Driven Apps 6.2 Creating and Configuring Views for Model-Driven Apps 6.3 Creating Generative Pages with Natural Language

7 Chapter 7: Composing, Securing, and Visualizing Model-Driven Apps

7.1 Composing a Model-Driven App 7.2 Configuring Access to Model-Driven Apps, Forms, and Views 7.3 Model-Driven App Charts and Dashboards

8 Chapter 8: Creating and Designing Canvas Apps

8.1 Creating a Canvas App Using Data 8.2 Designing for Accessibility, Performance, Responsiveness, and Usability 8.3 Automating Business Processes from Canvas Apps

9 Chapter 9: Canvas App Components, Variables, and Error Handling

9.1 Reusable Components, Named Formulas, and User-Defined Functions 9.2 Managing Variables and Collections 9.3 Implementing Error Handling in Canvas Apps

10 Chapter 10: Testing Canvas Apps and Creating Agents from Apps

10.1 Testing Canvas Apps with Monitor 10.2 Creating a Copilot Studio Agent from a Canvas App

11 Chapter 11: Cloud Flow Triggers, Connectors, and Approvals

11.1 Recommending Cloud Flow Triggers 11.2 Evaluating and Recommending Connectors 11.3 Managing Approvals with Cloud Flows

12 Chapter 12: Cloud Flow Actions, Control, and Troubleshooting

12.1 Configuring Cloud Flow Actions 12.2 Implementing Flow Control with Conditions and Loops 12.3 Testing and Troubleshooting Cloud Flows

13 Chapter 13: Building Prompts in AI Hub

13.1 Building Prompts from Templates or from Blank 13.2 Adding Inputs to a Prompt 13.3 Adding Knowledge to a Prompt

14 Chapter 14: Prompt Settings, Models, and Consuming AI

14.1 Customizing Prompt Settings and Models 14.2 Consuming Prompts in Apps and Cloud Flows 14.3 Consuming AI Models in Apps and Cloud Flows

15 Chapter 15: Business Rules and Business Process Flows

15.1 Configuring Business Rules 15.2 Configuring Business Process Flows

16 Chapter 16: Calculated Columns and Business Logic Strategy

16.1 Calculated, Rollup, and Formula Columns 16.2 Evaluating Use Cases for Business Logic

7.3 Model-Driven App Charts and Dashboards

Key Takeaways Explain This Topic AI

The chart designer supports column, bar, line, pie, funnel, and doughnut charts (plus tag and scatter variants), with series (aggregated values) plotted against categories (groupings)

Multi-series charts compare two or more aggregates side by side, stacked, or 100% stacked — and a chart always binds to a single view for its row filter

Charts exist as personal charts (user-owned, shareable) or system charts (maker-created, visible to all app users); both can be exported and imported as XML

Classic dashboards come in single-stream and multi-stream layouts; interactive dashboards add real-time filtering and stream tiles for service workloads at an awareness level

Use a chart for quick in-context visuals, a view for working a record list, and Power BI when you need cross-source analysis, rich visuals, or enterprise distribution

Last updated: July 2026

Charts and dashboards are how a model-driven app answers "how are we doing?" without leaving Dataverse. The exam tests the designer mechanics, the personal/system distinction, and — most of all — knowing when a native chart or dashboard is enough versus when you reach for Power BI.

The Chart Designer

Every chart is bound to one table and one view : the view supplies the row filter, the chart supplies the aggregation. Open a table's Charts area in the maker portal (or from a view grid, select the chart pane and New chart ) to launch the chart designer .

The designer's vocabulary:

Series (Legend Entries) — the numeric aggregate you plot: Count of records, or Sum/Average/Min/Max of a currency or number column (for example, Sum of Estimated Revenue).

Categories (Horizontal Axis) — the grouping column: Status, Owner, or a date grouped by month/week/day.

Chart types — Column, Bar, Line, Pie, Funnel, and Doughnut , plus Tag and Scatter variants. Funnel charts are the classic sales-pipeline shape; doughnut is a pie with a hole that can show a total in the center.

Multi-series charts add a second (or more) series — say, Count of Won Opportunities next to Count of Lost Opportunities by month — and let you render them side by side, stacked , or 100% stacked . A Top X rule (top 5, top 10) or Bottom X trims noisy categories.

Key limits to remember: one table per chart, one view per chart, and date grouping built in (day/week/month/quarter/year). Anything more exotic — multiple tables, calculated measures, drill-through across sources — is Power BI territory.

Personal vs. System Charts and XML

Personal charts are what end users build from a view grid. They are owned by that user, invisible to others until explicitly shared with users or teams, and can be reassigned.

System charts are created by makers in the table definition and ship with the app for everyone. Only users with sufficient privileges (System Administrator, System Customizer, or equivalent) create or edit them.

Both kinds can be exported as XML and re-imported — from the charts list, use Export chart / Import chart . At awareness level, know that the XML describes the series, categories, aggregates, and formatting, and that editing it by hand (or in tools like XrmToolBox) is the power-user route for features the visual designer hides, such as some multi-series formatting.

Dashboards

Classic Dataverse dashboards are layout canvases you drop components into. When you create one you pick a layout — 2-, 3-, or 4-column , in single-stream or multi-stream variants:

Single-stream dashboards emphasize one data stream (one view/list) alongside charts — the typical tier-1 agent wall: one queue of work plus supporting visuals.

Multi-stream dashboards tile several streams (multiple views from potentially different tables) with charts, giving a broader operational picture.

Components you can place on a classic dashboard:

Component What it shows
Chart Any personal or system chart
List A view's rows, selectable per stream
iFrame An embedded external web page
Web resource An HTML/Silverlight-era resource from Dataverse
Assistant Relationship insights cards (where licensed)
Power BI tile/report Embedded analytics (see below)

Interactive dashboards (the legacy "interactive experience" hubs used heavily in customer service) take streams further with real-time visual filtering : clicking a chart segment filters the stream tiles instantly, and streams support queue-style workloads. Know them at awareness level — their purpose (service triage), their stream tiles, and that they are configured separately from classic dashboards.

Dashboards can be personal (user-created and shareable) or system (maker-created in the app, visible per app and per role where the app restricts it). A dashboard can also be added to an app as a page and even set as the app's landing experience.

Embedding Power BI

Two native integrations exist:

Power BI tiles on a classic dashboard — a user with a Power BI license pins visuals from their Power BI dashboards into a Dataverse dashboard. The organization's admin must first enable Power BI embedding in the environment's settings (Power Platform admin center > environment > Settings > Features), and tiles respect Power BI security and licensing.

Power BI embedded reports/dashboards as full pages — makers can surface an entire Power BI report inside the app, with optional contextual filtering so the report filters to the current record or view.

Chart vs. View vs. Power BI

The exam's favorite decision question:

View — when the user must work the list : sort, filter, edit inline, run actions on rows. Views are the default working surface.

Chart — when a quick, in-context aggregate helps: pipeline by stage, cases by priority, counts this month. Fast, native, no extra licensing, but single-table and limited visuals.

Dashboard — when several charts/lists must be seen together as a landing page.

Power BI — when you need data from multiple sources , rich/custom visuals, complex measures (DAX), scheduled refresh, or enterprise-wide distribution beyond app users.

Scenario: an operations lead wants "open incidents by severity next to the list of incidents older than 7 days." A multi-stream classic dashboard with a severity column chart and an aged-incidents list answers it natively. If she later wants incident trends joined with finance data from an ERP, that is the Power BI signal.