This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Visualize and analyze the data (25–30%)
   --> Identify patterns and trends
      --> Use AI visuals

Note that there are 10 practice questions (with answers and explanations) at the end of each topic. Also, there are 2 practice tests with 60 questions each available on the hub below all the exam topics.

Overview

With the integration of AI capabilities into Power BI, report authors and analysts can now use AI visuals to uncover insights, identify patterns, detect anomalies, and explain outcomes—often without writing DAX or complex formulas. These features help accelerate exploratory analysis, data comprehension, and decision-making.

In the PL-300 exam, you may be asked to choose when to use AI visuals, understand what insights they produce, and recognize their requirements and limitations.

What Are AI Visuals?

AI visuals are special visual types or analysis tools powered by machine learning and statistical models embedded into Power BI. Instead of building raw visuals manually, AI visuals can automatically generate insights from the data behind your reports.

Core AI visuals and features in Power BI include:

• Key Influencers
• Decomposition Tree
• Anomaly Detection
• Explain the increase / decrease (via the Analyze feature)
• Text-based AI visuals (e.g., integration with Copilot / natural-language support)

These features help you identify patterns, trends, and drivers in your data—precisely the skills tested in this section of the PL-300 exam.

Key AI Visuals and Features

1. Key Influencers Visual

Purpose: Understand what factors most influence a measure or outcome.

What It Does:

• Ranks attributes based on influence (e.g., why customer churn is high)
• Shows effect sizes and how much each factor contributes
• Can work with both categorical and numeric fields

When to Use:

• You need to explain why values differ
• You want to drive business insights (e.g., why revenue varies by region)

2. Decomposition Tree

Purpose: Break down a key metric into its contributing components.

What It Does:

• Lets you drill into a measure across dimensions (e.g., sales by region → by product → by salesperson)
• Supports automatic ranking or AI-suggested splits
• Encourages exploratory and guided analysis

When to Use:

• You need a visual explanation of a hierarchical breakdown
• You want AI to suggest meaningful splits

3. Anomaly Detection

Purpose: Automatically identify unexpected spikes or dips in time-series visuals.

What It Does:

• Highlights data points significantly outside expected patterns
• Provides anomaly shading and explanations
• Supports sensitivity adjustments

When to Use:

• You are analyzing trends over time (e.g., daily web traffic)
• You want to flag outliers without manual inspection

4. Explain the Increase / Decrease

Purpose: Automatically explain why a value changed between two points.

What It Does:

• Produces AI-generated insights showing contributing dimensions
• Works from right-click context menus in visuals
• Helps uncover correlated patterns

When to Use:

• You’re tracking metric changes (e.g., month-to-month sales)
• You need quick narrative insights

5. Text-Based AI (Copilot / Natural Language)

Purpose: Generate narrative insights using natural language over data.

What It Does:

• Responds to prompts (e.g., “Explain sales trends by region”)
• Produces summaries, visuals, explanations
• Bridges analytic capability and user intent

When to Use:

• You want narrative context or augment analysis
• You seek a rapid, conversational interface for exploration

What AI Visuals Are Not

It’s important for the PL-300 exam to know limitations:

• AI visuals do not replace core modeling practices
• They don’t change underlying data
• Results depend on data quality and model design
• They may not be appropriate where business logic must be explicit and traceable

Requirements and Considerations

Data Requirements

• AI visuals often require numeric measures
• Proper data relationships improve outcomes
• Time-series visuals need continuous date/time

Permissions and Licensing

• Some AI capabilities (e.g., Copilot integration) may require appropriate licenses or tenant settings
• AI insights usually run on the Power BI Service, not just Desktop

Performance

• Complex visuals or large datasets may take longer to analyze
• AI visuals should be used judiciously in operational dashboards

Best Practices for PL-300

• Use AI visuals to accelerate exploration, not replace fundamental analysis
• Always validate AI-generated insights with business knowledge
• Know when an AI visual like Key Influencers is more suitable than a Decomposition Tree
• Combine AI visuals with traditional visuals for storytelling completeness
• Recognize exam scenarios that describe why something changed or what influences an outcome — these often point to AI features

PL-300 Exam Scenarios to Expect

You might see scenarios like:

• “Users need to understand why a metric changed significantly month over month.”
  → Explain the increase or Key Influencers
• “A manager wants to break down profitability by business units to find contributing drivers.”
  → Decomposition Tree
• “There’s a sudden spike in orders that requires automated detection.”
  → Anomaly Detection
• “Users want narrative summaries without writing DAX.”
  → Text-based AI / Copilot analysis

Summary

AI visuals in Power BI offer powerful ways to identify patterns, trends, and drivers without deep technical overhead. Key components include:

• Key Influencers
• Decomposition Tree
• Anomaly Detection
• Explain the increase / decrease
• Text-based AI interfaces

For the PL-300 exam, focus on:

✔ When to use each AI feature
✔ What insights they provide
✔ Their data requirements
✔ Their limitations

Understanding the right tool for the right scenario is critical both in the exam and in real-world Power BI work.

Practice Questions

Go to the Practice Questions for this topic.