PL-300T00: Design and manage analytics solutions using Power BI

Instructor-led Microsoft Power BI training for data analysts, BI professionals, report developers, business analysts, and technical users who need to prepare, model, visualize, analyze, manage, and secure data with Microsoft Power BI.

Microsoft’s current course page lists the course as PL-300T00: Design and manage analytics solutions using Power BI. The user-requested code APL-300T00 appears to align to the same Power BI Data Analyst course and PL-300 certification path. Microsoft describes this course as covering methods and best practices for modeling, visualizing, and analyzing data with Power BI, including data access, processing, report deployment, dashboards, sharing, and content distribution.

Certification URL: https://learn.microsoft.com/en-us/credentials/certifications/data-analyst-associate/
Study guide URL: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/pl-300
Audience: Data analysts, BI analysts, Power BI report authors, business analysts, analytics professionals, and Microsoft data platform users.

Why choose Dynamics Edge for APL-300T00 training?

Dynamics Edge delivers Microsoft Power BI training with a practical business reporting and analytics focus. This course can be delivered as public instructor-led training, private team training, government training, or customized Power BI adoption training.

• Learn how to prepare, model, visualize, analyze, manage, and secure Power BI content.
• Build practical skills with Power BI Desktop, Power Query, DAX, semantic models, reports, workspaces, apps, and row-level security.
• Prepare for the Microsoft Certified: Power BI Data Analyst Associate certification and Exam PL-300.
• Apply Power BI skills to real reporting, dashboard, governance, and self-service analytics scenarios.
• Customize the class for finance, operations, sales, customer service, government, or executive reporting teams.

What will you learn in PL-300T00 training?

This course teaches students how to deliver actionable insights using Microsoft Power BI. Microsoft states that Power BI data analysts work with business stakeholders, analytics engineers, and data engineers to identify requirements, acquire data, prepare data, model data, visualize and analyze data, and manage and secure Power BI. Microsoft also states that candidates should be proficient with Power Query and Data Analysis Expressions, or DAX.

• Connect to relational, non-relational, cloud, and on-premises data sources.
• Clean, transform, shape, and load data with Power Query.
• Design semantic models, relationships, hierarchies, calculations, and DAX measures.
• Build interactive Power BI reports, dashboards, analytics experiences, and storytelling visuals.
• Manage workspaces, apps, refresh, gateways, governance, and row-level security.

Course Outline: Design and manage analytics solutions using Power BI

Module 1: Get started with Microsoft data analytics

Students begin by learning the role of the data analyst and how Power BI supports end-to-end analytics. This module introduces the Power BI environment, the flow of data through Power BI Desktop and Power BI service, and the relationship between Power BI and Microsoft Fabric.

Topics include:

• Data analyst responsibilities and common data roles.
• Descriptive, diagnostic, predictive, prescriptive, and AI-assisted analysis.
• Power BI Desktop, Power BI service, and Power BI Mobile.
• Semantic models, visualizations, reports, dashboards, and tiles.
• Microsoft Fabric and end-to-end analytics integration.

Module 2: Get data in Power BI

Students learn how to connect Power BI to data sources and evaluate source data before building reports. This module introduces connection options, storage modes, data profiling, privacy settings, and common import issues.

Topics include:

• Connect to files, databases, cloud services, and shared semantic models.
• Choose between Import, DirectQuery, DirectLake, and other connection options.
• Configure data source settings, credentials, and privacy levels.
• Profile data by reviewing column quality, distribution, and statistics.
• Resolve data import errors and improve query performance.

Module 3: Clean, transform, and load data in Power BI

Students use Power Query to clean, shape, transform, and prepare data for modeling. This module focuses on data quality, naming conventions, query structure, and performance-aware transformation choices.

Topics include:

• Remove duplicates, errors, nulls, inconsistencies, and unwanted rows or columns.
• Select appropriate data types and apply user-friendly column names.
• Split, merge, append, pivot, unpivot, group, and aggregate data.
• Create fact tables, dimension tables, and relationship keys.
• Configure query loading and improve Power Query performance.

Module 4: Configure a semantic model in Power BI

Students learn how to design a semantic model that supports accurate reporting and analysis. This module covers table relationships, cardinality, cross-filter direction, hierarchies, model properties, and calculation readiness.

Topics include:

• Configure table, column, and field properties.
• Create and manage relationships between fact and dimension tables.
• Define cardinality, cross-filter direction, and role-playing dimensions.
• Create hierarchies, date tables, and model-friendly structures.
• Use quick measures, numeric range parameters, and field parameters.

Module 5: Create DAX calculations in semantic models

Students learn how to use Data Analysis Expressions to create calculated tables, calculated columns, and measures. This module introduces DAX syntax, common functions, row context, filter context, and iterator functions.

Topics include:

• Understand DAX syntax, functions, operators, and expressions.
• Create calculated tables and calculated columns.
• Create explicit measures for reusable business calculations.
• Use common DAX functions such as SUM, FILTER, CALCULATE, DIVIDE, and FORMAT.
• Apply iterator functions such as SUMX, AVERAGEX, COUNTX, MINX, and MAXX.

Module 6: Modify DAX filter context in Power BI

Students learn how filter context affects measures and how to modify that context with DAX. This module emphasizes CALCULATE, inactive relationships, context transition, and correct measure behavior in visuals.

Topics include:

• Understand row context, filter context, and evaluation context.
• Use CALCULATE to modify filter context.
• Apply filter removal and filter replacement patterns.
• Use inactive relationships in DAX calculations.
• Perform context transition for advanced measure logic.

Module 7: Use DAX time intelligence functions in Power BI

Students learn how to build date-aware calculations for common business reporting scenarios. This module covers custom date tables, marked date tables, and DAX time intelligence measures.

Topics include:

• Create and configure a custom date table.
• Mark a table as the official date table.
• Build year-to-date, quarter-to-date, and month-to-date measures.
• Compare current period, prior period, and same-period-last-year results.
• Apply time intelligence correctly in semantic models.

Module 8: Create visual calculations in Power BI

Students learn how visual calculations differ from semantic model measures. This module introduces visual-level DAX calculations that support analysis directly in report visuals.

Topics include:

• Understand the difference between measures and visual calculations.
• Create visual calculations in Power BI Desktop.
• Use visual calculation functions with axes and reset behavior.
• Hide fields and simplify the report authoring experience.
• Apply visual calculations to improve analysis inside reports.

Module 9: Design Power BI reports

Students learn how to design effective reports that communicate business insights clearly. This module focuses on report structure, visual selection, formatting, filtering, and report page design.

Topics include:

• Select appropriate visuals for business questions and data types.
• Format visuals, report pages, titles, labels, themes, and backgrounds.
• Use slicers, filters, and the Filters pane.
• Build multi-page reports with clear layout and report flow.
• Publish and interact with reports in the Power BI service.

Module 10: Enhance Power BI report designs for the user experience

Students learn how to improve report usability, storytelling, navigation, and accessibility. This module covers report interactions, bookmarks, tooltips, drillthrough, mobile layouts, and app-like navigation.

Topics include:

• Configure bookmarks, buttons, shapes, and report navigation.
• Add custom tooltips, visual headers, and drillthrough pages.
• Configure interactions, sync slicers, sorting, grouping, and layering.
• Design mobile-optimized report views.
• Improve accessibility, storytelling, and user experience.

Module 11: Perform analytics in Power BI

Students learn how to identify patterns, trends, anomalies, and business insights in Power BI. This module includes Power BI analytics features, forecasting, clustering, AI visuals, and what-if analysis.

Topics include:

• Use the Analyze feature to explain increases, decreases, and differences.
• Group, bin, and cluster data.
• Identify outliers, anomalies, patterns, and trends.
• Use forecasting, reference lines, and what-if parameters.
• Apply AI visuals and analytics features to support insight discovery.

Module 12: Use Copilot in Power BI

Students learn how Copilot can support report development and data exploration in Power BI. This module emphasizes that Copilot improves productivity but does not replace data analysis judgment, governance, or model quality.

Topics include:

• Use Copilot during Power BI report development.
• Prepare semantic models for AI-assisted reporting.
• Ask natural-language questions about data.
• Generate report page ideas and summaries.
• Review Copilot output for accuracy, business context, and data quality.

Module 13: Optimize a model for performance in Power BI

Students learn how to improve model, report, and DAX performance. This module focuses on reducing unnecessary data, improving calculations, reviewing metadata, and using performance tools.

Topics include:

• Remove unnecessary rows, columns, and high-cardinality fields.
• Use variables to improve DAX readability and performance.
• Review measure, relationship, and visual performance.
• Use Performance Analyzer and DAX query tools.
• Reduce granularity and optimize model design.

Module 14: Manage workspaces and items in Power BI

Students learn how to use the Power BI service to organize, publish, collaborate, and manage analytics assets. This module covers workspace configuration, access control, publishing, and service building blocks.

Topics include:

• Describe Power BI service building blocks.
• Create and configure workspaces.
• Assign workspace roles and manage access.
• Publish reports and semantic models to the Power BI service.
• Manage reports, dashboards, semantic models, and related items.

Module 15: Manage semantic models in Power BI

Students learn how to manage semantic models after publication. This module covers gateways, scheduled refresh, incremental refresh, lineage, impact analysis, promotion, certification, and query caching.

Topics include:

• Identify when a data gateway is required.
• Configure scheduled refresh and refresh credentials.
• Configure incremental refresh with RangeStart and RangeEnd.
• Promote and certify trusted semantic models.
• Use lineage view, impact analysis, and query caching.

Module 16: Create and manage Power BI apps

Students learn how to distribute Power BI content to users and manage analytics delivery. This module explains content distribution methods, Power BI apps, governance, subscriptions, and usage tracking.

Topics include:

• Choose appropriate content distribution methods.
• Create and update Power BI apps.
• Package related reports, dashboards, and semantic models for users.
• Apply governance principles to shared content.
• Track reports, dashboards, subscriptions, and data alerts.

Module 17: Secure data access in Power BI

Students learn how to secure Power BI content and data access. This module focuses on workspace permissions, semantic model access, static row-level security, dynamic row-level security, and sensitivity labeling.

Topics include:

• Assign workspace roles and item-level access.
• Configure access to reports, apps, dashboards, and semantic models.
• Create static row-level security roles.
• Create dynamic row-level security with DAX functions.
• Apply sensitivity labels and governance controls.

Certification Alignment

This course aligns to Microsoft Certified: Power BI Data Analyst Associate and Exam PL-300: Microsoft Power BI Data Analyst. Microsoft lists the certification as intermediate level, for the Power BI product, with the Data Analyst role and Data analytics subject area.

Microsoft’s PL-300 study guide lists the current skills measured as of April 20, 2026 as:

• Prepare the data: 25–30%.
• Model the data: 25–30%.
• Visualize and analyze the data: 25–30%.
• Manage and secure Power BI: 15–20%.

Course Review

By the end of this course, students should be able to connect to data, clean and transform data, design Power BI semantic models, create DAX calculations, build interactive reports, use analytics and Copilot features, manage Power BI service content, configure refresh, distribute apps, and secure data with row-level security.

Certification Exam Review

This course supports preparation for Exam PL-300 by covering the full analytics lifecycle in Power BI: preparing data, modeling data, visualizing and analyzing data, and managing and securing Power BI assets. Students should use the Microsoft study guide, practice assessment, and hands-on Power BI labs to review skills before scheduling the exam.