PL-300: Microsoft Power BI Data Analyst

Connect Power BI Desktop to a SQL Server source, pull in the AdventureWorks reseller sales data, and shape it into a clean, load-ready dataset. You'll profile columns for quality, split and merge tables, unpivot wide layouts, append CSV sources, and set explicit types before load. You'll finish with a Power BI semantic model that's ready for modeling and DAX.

Turn a set of loaded tables into a production-quality Star Schema. You'll verify relationship cardinality, mark the date table so time intelligence works, build hierarchies for drilling, handle role-playing dimensions with USERELATIONSHIP, tune column properties, and hide the model's plumbing from report authors. You'll close by asking Copilot to summarize the shape you built.

Build the DAX layer that drives the AdventureWorks reports. You'll author explicit measures for the core sales KPIs, iterate over rows with SUMX and AVERAGEX, modify filter context with CALCULATE, and apply time-intelligence patterns for YTD, YoY, and MoM analysis. You'll also generate and verify a measure using Copilot in DAX query view, then organize the measures with display folders.

Practice Power BI's visual calculations — DAX shorthand that lives on the visual instead of the semantic model. You'll drop a matrix, then add running totals with RUNNINGSUM, moving averages with MOVINGAVERAGE, and month-over-month deltas with PREVIOUS. You'll learn when a calculation belongs on the model vs. on the visual, and apply conditional formatting to make the deltas readable at a glance.

Build a three-page Power BI report against the AdventureWorks semantic model. You'll insert dropdown and list slicers, line-and-column, stacked column, stacked bar, matrix and multi-row card visuals, apply page-level filters, format visuals for contrast and readability, and synchronise slicers so the pages stay consistent with one another.

Turn a base report into an interactive analytical experience. You'll build a drillthrough page, capture views with bookmarks, drive conditional formatting from a companion CSV, tune a Q&A visual with synonyms, ask Copilot to summarize the semantic model, and apply Power BI's built-in analytics — forecast, decomposition tree, and key influencers. You'll republish to the Service and test the interactions in the browser.

Configure Row-Level Security on the AdventureWorks semantic model. You'll create a dynamic RLS role that filters by the signed-in user's identity via USERPRINCIPALNAME, a static RLS role that pins a regional scope, and test each one with View As. You'll explore how overlapping roles behave, and see where role membership is assigned once the model is published to the Power BI Service.