Tag: Microsoft Copilot

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 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.