Microsoft Power BI Data Analyst PL300 Certification 2025

Microsoft Power BI Data Analyst PL300 Certification 2025, Certification Exam Questions & Answers preparation 2025

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Microsoft Power BI Data Analyst PL300 Test Exam

Audience profile

Candidates for this exam deliver actionable insights by working with available data and applying domain expertise. They provide meaningful business value through easy-to-comprehend data visualizations, enable others to perform self-service analytics, and deploy and configure solutions for consumption.

The Power BI data analyst works closely with business stakeholders to identify business requirements. They collaborate with enterprise data analysts and data engineers to identify and acquire data. They also transform the data, create data models, visualize data, and share assets by using Power BI.

Candidates for this exam should be proficient at using Power Query and writing expressions by using Data Analysis Expressions (DAX). These professionals know how to assess data quality. Plus, they understand data security, including row-level security and data sensitivity.

• Prepare the data (25–30%)

• Model the data (25–30%)

• Visualize and analyze the data (25–30%)

Deploy and maintain assets (15–20%)

Prepare the data (25–30%)

Get data from data sources

  • Identify and connect to a data source

  • Change data source settings, including credentials, privacy levels, and data source locations

  • Select a shared dataset, or create a local dataset

  • Choose between DirectQuery, Import, and Dual mode

  • Change the value in a parameter Clean the data

  • Evaluate data, including data statistics and column properties

  • Resolve inconsistencies, unexpected or null values, and data quality issues

  • Resolve data import errors Transform and load the data

  • Select appropriate column data types

  • Create and transform columns

  • Transform a query

  • Design a star schema that contains facts and dimensions

  • Identify when to use reference or duplicate queries and the resulting impact

  • Merge and append queries

  • Identify and create appropriate keys for relationships

  • Configure data loading for queries

Model the data (25–30%)

Design and implement a data model

  • Configure table and column properties

  • Implement role-playing dimensions

  • Define a relationship's cardinality and cross-filter direction

  • Create a common date table

  • Implement row-level security roles

Create model calculations by using DAX

  • Create single aggregation measures

  • Use CALCULATE to manipulate filters

  • Implement time intelligence measures

  • Identify implicit measures and replace with explicit measures

  • Use basic statistical functions

  • Create semi-additive measures

Create a measure by using quick measures

  • Create calculated tables

Optimize model performance

  • Improve performance by identifying and removing unnecessary rows and columns

  • Identify poorly performing measures, relationships, and visuals by using Performance Analyzer

  • Improve performance by choosing optimal data types

  • Improve performance by summarizing data

Visualize and analyze the data (25–30%)

Create reports

• Identify and implement appropriate visualizations

• Format and configure visualizations

• Use a custom visual

• Apply and customize a theme

• Configure conditional formatting

• Apply slicing and filtering

• Configure the report page

• Use the Analyze in Excel feature

• Choose when to use a paginated report

Enhance reports for usability and storytelling

• Configure bookmarks

• Create custom tooltips

• Edit and configure interactions between visuals

• Configure navigation for a report

• Apply sorting

• Configure sync slicers

• Group and layer visuals by using the Selection pane

• Drill down into data using interactive visuals

• Configure export of report content, and perform an export

• Design reports for mobile devices

• Incorporate the Q&A feature in a report

Identify patterns and trends

• Use the Analyze feature in Power BI

• Use grouping, binning, and clustering

• Use AI visuals

• Use reference lines, error bars, and forecasting

• Detect outliers and anomalies

Create and share scorecards and metrics

Deploy and maintain assets (15–20%)

Create and manage workspaces and assets

  • Create and configure a workspace

  • Assign workspace roles

  • Configure and update a workspace app

  • Publish, import, or update assets in a workspace

  • Create dashboards

  • Choose a distribution method

  • Apply sensitivity labels to workspace content

  • Configure subscriptions and data alerts

  • Promote or certify Power BI content

  • Manage global options for files

Manage datasets

  • Identify when a gateway is required

  • Configure a dataset scheduled refresh

  • Configure row-level security group membership

  • Provide access to datasets