Tag: Microsoft Copilot

AI, Business Intelligence, Data Modeling, Microsoft Certification, PL-300, Power BI

Use Copilot to Summarize the Underlying Semantic Model (PL-300 Exam Prep)

 
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 Copilot to Summarize the Underlying Semantic Model

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

As part of the Visualize and analyze the data (25–30%) exam domain—specifically Identify patterns and trends—PL-300 candidates are expected to understand how Copilot in Power BI can be used to quickly generate insights and summaries from the semantic model.

Copilot helps analysts and business users understand datasets faster by automatically explaining the structure, measures, relationships, and high-level patterns present in a Power BI model—without requiring deep manual exploration.

What Is the Semantic Model in Power BI?

The semantic model (formerly known as a dataset) represents the logical layer of Power BI and includes:

  • Tables and columns
  • Relationships between tables
  • Measures and calculated columns (DAX)
  • Hierarchies
  • Metadata such as data types and formatting

Copilot uses this semantic layer—not raw source systems—to generate summaries and insights.

What Does Copilot Do When Summarizing a Semantic Model?

When you ask Copilot to summarize a semantic model, it can:

  • Describe the purpose and structure of the model
  • Identify key tables and relationships
  • Explain important measures and metrics
  • Highlight common business themes (such as sales, finance, operations)
  • Surface high-level trends and patterns present in the data

This is especially useful for:

  • New analysts onboarding to an existing model
  • Business users exploring a report for the first time
  • Quickly validating model design and intent

Where and How Copilot Is Used in Power BI

Copilot can be accessed in Power BI through supported experiences such as:

  • Power BI Service (Fabric-enabled environments)
  • Report authoring and exploration contexts
  • Q&A-style prompts written in natural language

Typical prompts might include:

  • “Summarize this dataset”
  • “Explain what this model is used for”
  • “What are the key metrics in this report?”

Copilot responds using natural language explanations, not DAX or SQL code.

Requirements and Considerations

For exam awareness, it’s important to understand that Copilot:

  • Requires Power BI Copilot to be enabled in the tenant
  • Uses the semantic model metadata and data the user has access to
  • Does not modify the model or data
  • Reflects existing security and permissions

Copilot is an assistive AI feature, not a replacement for proper model design or validation.

Business Value of Semantic Model Summarization

Using Copilot to summarize a semantic model helps organizations:

  • Reduce time spent understanding complex datasets
  • Improve data literacy across business users
  • Enable faster insight discovery
  • Support storytelling by clearly explaining what the data represents

From an exam perspective, Microsoft emphasizes usability, insight generation, and decision support.

Exam-Relevant Scenarios

You may see PL-300 questions that ask you to:

  • Identify when Copilot is the best tool to explain a dataset
  • Distinguish Copilot summaries from visuals or DAX-based analysis
  • Recognize Copilot as a descriptive and exploratory tool
  • Understand limitations related to permissions and availability

Remember: Copilot summarizes and explains—it does not cleanse data, create relationships, or replace modeling skills.

Key Takeaways for PL-300

✔ Copilot summarizes the semantic model, not source systems
✔ It uses natural language to explain structure and insights
✔ It supports pattern identification and exploration
✔ It enhances usability and storytelling, not data modeling
✔ Permissions and tenant settings still apply

Practice Questions

Go to the Practice Questions for this topic.

Analytics, Artificial Intelligence (AI), Data Visualization, Microsoft Certification, PL-300, Power BI

Use Copilot to Suggest Content for a New Report Page (PL-300 Exam Prep)

 
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%)
   --> Create reports
      --> Use Copilot to Suggest Content for a New Report Page

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.

Where This Topic Fits in the Exam

The PL-300: Microsoft Power BI Data Analyst exam tests your ability to design effective, insightful reports using both traditional and AI-assisted features. The skill “Use Copilot to suggest content for a new report page” appears under Create reports, highlighting Microsoft’s expectation that modern analysts understand how AI can assist—but not replace—human judgment in report design.

This topic is closely related to (but distinct from):

  • Use Copilot to create a new report page
  • Create a narrative visual with Copilot

For exam purposes, the key distinction is that Copilot is suggesting ideas, not automatically building a finalized page.

What Does “Suggest Content” Mean in Power BI Copilot?

When Copilot suggests content for a new report page, it:

  • Analyzes the existing semantic model (tables, relationships, measures)
  • Interprets a natural language request or business goal
  • Recommends:
    • Visual types (e.g., bar charts, KPIs, tables)
    • Relevant fields or measures
    • Possible analytical focus areas (trends, comparisons, summaries)

Unlike fully creating a page, Copilot may not automatically place all visuals on the canvas. Instead, it provides guidance and recommendations that the analyst can choose to implement.

Why This Matters for PL-300

Microsoft includes this topic to ensure candidates understand:

  • The assistive role of Copilot in report design
  • How AI can help analysts decide what to show, not just how to show it
  • That Copilot suggestions still require validation and refinement

On the exam, this topic is about decision support, not automation.

Typical Use Cases for Content Suggestions

Copilot is especially useful when:

  • You are unsure which visuals best represent a business question
  • You want guidance on common analytical patterns (e.g., trends, breakdowns, comparisons)
  • You need inspiration for structuring a new report page quickly
  • You are working with a well-modeled dataset but lack domain familiarity

Example scenarios:

  • Suggesting visuals for sales performance analysis
  • Recommending KPIs for executive summaries
  • Identifying common breakdowns such as region, product, or time

How Copilot Generates Suggestions

Copilot bases its suggestions on:

  • Table and column names
  • Defined measures and calculations
  • Relationships in the model
  • Metadata and semantic structure

Because of this, model quality directly impacts suggestion quality. Poor naming or unclear measures lead to weaker recommendations.

What Copilot Does Well

Copilot excels at:

  • Identifying commonly used measures
  • Recommending standard visual patterns
  • Highlighting trends, totals, and comparisons
  • Accelerating the “what should I show?” phase of report creation

This makes it ideal for early-stage report design.

What Copilot Does Not Do

Copilot does not:

  • Understand nuanced business definitions
  • Guarantee the most relevant KPIs
  • Validate measure logic or calculations
  • Decide final layout or storytelling flow
  • Replace analyst expertise

For the exam, it’s critical to recognize that Copilot suggestions are optional and advisory.

Copilot Suggestions vs Manual Design

AspectCopilot SuggestionsManual Design
PurposeGuidance and ideasFinal decisions
SpeedFastSlower
PrecisionGeneralizedExact
ResponsibilityAnalyst reviewsAnalyst defines

PL-300 scenarios often test whether you know when to accept Copilot guidance and when manual expertise is required.

Best Practices When Using Copilot Suggestions

From an exam and real-world perspective:

  • Treat suggestions as starting points
  • Validate relevance against business goals
  • Confirm measures and aggregations
  • Adjust visuals, filters, and layout manually
  • Ensure suggested content aligns with stakeholder needs

Copilot helps with ideation, not accountability.

Exam Focus — How This Topic Is Tested

PL-300 questions typically:

  • Ask when Copilot should be used to suggest content
  • Contrast suggesting content vs creating content
  • Test understanding of Copilot’s advisory role
  • Emphasize the importance of analyst judgment

Common exam phrasing:

  • “Which feature can recommend visuals for a new report page?”
  • “Which tool helps identify relevant content without automatically building the page?”

Correct answers often point to Copilot, with the understanding that the analyst still curates the final result.

Summary

For “Use Copilot to suggest content for a new report page”, you should understand:

  • Copilot provides recommendations, not finalized pages
  • Suggestions are based on the semantic model
  • Output quality depends on model design
  • Analyst review and decision-making remain essential
  • This feature accelerates ideation and planning in report creation

This topic reinforces Microsoft’s view of Copilot as an AI assistant for analysts, not a replacement—an important mindset for both the PL-300 exam and real-world Power BI development.

Practice Questions

Go to the practice questions for this topic.

Analytics, Data Visualization, Microsoft Certification, PL-300, Power BI

Use Copilot to Create a New Report Page (PL-300 Exam Prep)

 
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%)
   --> Create reports
      --> Use Copilot to Create a New Report Page

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.

Where This Topic Fits in the Exam

The PL-300: Microsoft Power BI Data Analyst exam increasingly emphasizes modern report authoring features, including the use of Copilot. Within the Create reports skill area, this topic evaluates your understanding of how AI-assisted tools can accelerate report creation while still requiring analyst judgment to validate results.

You are not tested on Copilot prompt engineering in depth, but rather on:

  • What Copilot can do
  • When it should be used
  • Its prerequisites and limitations
  • How it fits into the report-building workflow

What Is Copilot in Power BI?

Copilot in Power BI is an AI-powered assistant that helps report authors generate content using natural language prompts. When used to create a new report page, Copilot can:

  • Automatically add a new page to an existing report
  • Suggest and place visuals based on the data model
  • Select fields, measures, and basic layouts
  • Apply default formatting and titles

Copilot accelerates report creation but does not replace the analyst’s responsibility for data accuracy, business logic, or design refinement.

What Does “Create a New Report Page with Copilot” Mean?

Using Copilot to create a new report page typically involves:

  • Prompting Copilot with a business question or request
    (for example, asking for a page that analyzes sales performance)
  • Allowing Copilot to generate:
    • A new page
    • One or more visuals
    • Suggested fields and aggregations
  • Reviewing, editing, and refining the generated content

The resulting page is a starting point, not a finished product.

Why This Matters for PL-300

Microsoft includes Copilot topics to ensure analysts understand:

  • How AI can speed up report authoring
  • The boundaries of AI-generated content
  • When manual intervention is still required

Exam scenarios often frame Copilot as a productivity tool, not a source of authoritative analysis.

Prerequisites and Requirements

To use Copilot in Power BI:

  • The tenant must have Copilot enabled
  • The user must have appropriate Power BI licensing
  • The dataset must be compatible and accessible
  • The data model should be well-designed with:
    • Clear table and column names
    • Proper relationships
    • Meaningful measures

A poorly modeled dataset will lead to poor Copilot output.

What Copilot Does Well

Copilot is well suited for:

  • Quickly scaffolding a new report page
  • Generating common business visuals (charts, tables, KPIs)
  • Suggesting relevant fields and measures
  • Helping users get started faster

It excels when:

  • The data model is clean and intuitive
  • The business request is high-level
  • Speed is more important than precision in the first draft

What Copilot Does Not Do

Copilot does not:

  • Validate business definitions
  • Guarantee correct aggregations
  • Replace DAX expertise
  • Understand nuanced business rules
  • Automatically optimize report performance

For the exam, it’s important to recognize that Copilot output must be reviewed and adjusted.

Copilot vs Manual Report Creation

AspectCopilotManual
SpeedVery fastSlower
ControlLower initiallyFull
AccuracyDepends on modelAnalyst-defined
Best useFirst draftFinal refinement

PL-300 scenarios often expect you to choose Copilot when rapid report creation is required, not when precision logic must be built from scratch.

Best Practices When Using Copilot

From an exam and real-world perspective:

  • Use Copilot to accelerate, not finalize
  • Always validate fields, filters, and aggregations
  • Refine visual types and formatting manually
  • Ensure the page aligns with business goals and storytelling

Copilot should be viewed as an assistant, not an authority.

Exam Focus — How This Topic Is Tested

PL-300 questions typically:

  • Ask when Copilot is an appropriate choice
  • Test understanding of Copilot’s role in report creation
  • Contrast Copilot-generated pages with manual design
  • Emphasize the need for review and refinement

Example exam framing:

“A user wants to quickly create a new report page summarizing key metrics. Which feature should they use?”

The correct answer often involves Copilot, followed by analyst validation.

Summary

For the Use Copilot to create a new report page topic, you should understand:

  • What Copilot can generate automatically
  • The requirements for using Copilot
  • Its strengths and limitations
  • How it fits into the report-authoring lifecycle
  • Why analyst oversight is still required

This topic reflects Microsoft’s direction toward AI-assisted analytics, while reinforcing that strong data modeling and visualization skills remain essential for PL-300 success.

Practice Questions

Go to the Practice Exam Questions for this topic.

AI, AI Governance, AI Strategy, Analytics, Artificial Intelligence (AI), Cloud computing, Computer Vision, Data Analysis, Data Careers, Data Education & Training, Data Governance, Data Integration, Data Strategy, Deep Learning, Generative AI, HR analytics, Large Language Models (LLMs), Machine Learning (ML), Natural Language Processing (NLP), People Analytics, Predictive Analytics, Workday, Workforce Analytics

AI in Human Resources: From Administrative Support to Strategic Workforce Intelligence
“AI in …” series

Human Resources has always been about people—but it’s also about data: skills, performance, engagement, compensation, and workforce planning. As organizations grow more complex and talent markets tighten, HR teams are being asked to move faster, be more predictive, and deliver better employee experiences at scale.

AI is increasingly the engine enabling that shift. From recruiting and onboarding to learning, engagement, and workforce planning, AI is transforming how HR operates and how employees experience work.

How AI Is Being Used in Human Resources Today

AI is now embedded across the end-to-end employee lifecycle:

Talent Acquisition & Recruiting

  • LinkedIn Talent Solutions uses AI to match candidates to roles based on skills, experience, and career intent.
  • Workday Recruiting and SAP SuccessFactors apply machine learning to rank candidates and surface best-fit applicants.
  • Paradox (Olivia) uses conversational AI to automate candidate screening, scheduling, and frontline hiring at scale.

Resume Screening & Skills Matching

  • Eightfold AI and HiredScore use deep learning to infer skills, reduce bias, and match candidates to open roles and future opportunities.
  • AI shifts recruiting from keyword matching to skills-based hiring.

Employee Onboarding & HR Service Delivery

  • ServiceNow HR Service Delivery uses AI chatbots to answer employee questions, guide onboarding, and route HR cases.
  • Microsoft Copilot for HR scenarios help managers draft job descriptions, onboarding plans, and performance feedback.

Learning & Development

  • Degreed and Cornerstone AI recommend personalized learning paths based on role, skills gaps, and career goals.
  • AI-driven content curation adapts as employee skills evolve.

Performance Management & Engagement

  • Betterworks and Lattice use AI to analyze feedback, goal progress, and engagement signals.
  • Sentiment analysis helps HR identify burnout risks or morale issues early.

Workforce Planning & Attrition Prediction

  • Visier applies AI to predict attrition risk, model workforce scenarios, and support strategic planning.
  • HR leaders use AI insights to proactively retain key talent.

Those are just a few examples of AI tools and scenarios in use. There are a lot more AI solutions for HR out there!

Tools, Technologies, and Forms of AI in Use

HR AI platforms combine people data with advanced analytics:

  • Machine Learning & Predictive Analytics
    Used for attrition prediction, candidate ranking, and workforce forecasting.
  • Natural Language Processing (NLP)
    Powers resume parsing, sentiment analysis, chatbots, and document generation.
  • Generative AI & Large Language Models (LLMs)
    Used to generate job descriptions, interview questions, learning content, and policy summaries.
    • Examples: Workday AI, Microsoft Copilot, Google Duet AI, ChatGPT for HR workflows
  • Skills Ontologies & Graph AI
    Used by platforms like Eightfold AI to map skills across roles and career paths.
  • HR AI Platforms
    • Workday AI
    • SAP SuccessFactors Joule
    • Oracle HCM AI
    • UKG Bryte AI

And there are AI tools being used across the entire employee lifecycle.

Benefits Organizations Are Realizing

Companies using AI effectively in HR are seeing meaningful benefits:

  • Faster Time-to-Hire and reduced recruiting costs
  • Improved Candidate and Employee Experience
  • More Objective, Skills-Based Decisions
  • Higher Retention through proactive interventions
  • Scalable HR Operations without proportional headcount growth
  • Better Strategic Workforce Planning

AI allows HR teams to spend less time on manual tasks and more time on high-impact, people-centered work.

Pitfalls and Challenges

AI in HR also carries significant risks if not implemented carefully:

Bias and Fairness Concerns

  • Poorly designed models can reinforce historical bias in hiring, promotion, or pay decisions.

Transparency and Explainability

  • Employees and regulators increasingly demand clarity on how AI-driven decisions are made.

Data Privacy and Trust

  • HR data is deeply personal; misuse or breaches can erode employee trust quickly.

Over-Automation

  • Excessive reliance on AI can make HR feel impersonal, especially in sensitive situations.

Failed AI Projects

  • Some initiatives fail because they focus on automation without aligning to HR strategy or culture.

Where AI Is Headed in Human Resources

The future of AI in HR is more strategic, personalized, and collaborative:

  • AI as an HR Copilot
    Assisting HR partners and managers with decisions, documentation, and insights in real time.
  • Skills-Centric Organizations
    AI continuously mapping skills supply and demand across the enterprise.
  • Personalized Employee Journeys
    Tailored learning, career paths, and engagement strategies.
  • Predictive Workforce Strategy
    AI modeling future talent needs based on business scenarios.
  • Responsible and Governed AI
    Stronger emphasis on ethics, explainability, and compliance.

How Companies Can Gain an Advantage with AI in HR

To use AI as a competitive advantage, organizations should:

  1. Start with High-Trust Use Cases
    Recruiting efficiency, learning recommendations, and HR service automation often deliver fast wins.
  2. Invest in Clean, Integrated People Data
    AI effectiveness depends on accurate and well-governed HR data.
  3. Design for Fairness and Transparency
    Bias testing and explainability should be built in from day one.
  4. Keep Humans in the Loop
    AI should inform decisions—not make them in isolation.
  5. Upskill HR Teams
    AI-literate HR professionals can better interpret insights and guide leaders.
  6. Align AI with Culture and Values
    Technology should reinforce—not undermine—the employee experience.

Final Thoughts

AI is reshaping Human Resources from a transactional function into a strategic engine for talent, culture, and growth. The organizations that succeed won’t be those that automate HR the most—but those that use AI to make work more human, more fair, and more aligned with business outcomes.

In HR, AI isn’t about replacing people—it’s about improving efficiency, elevating the candidate and employee experiences, and helping employees thrive.