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intermediate

PL-300: Power BI Data Analyst — Exam Prep for Developers

A preparation course for Microsoft exam PL-300 (Microsoft Power BI Data Analyst), written for senior developers in Big 4 and other professional-services firms who already think in SQL and code — and keep getting surprised by DAX. The course follows the official skills outline (April 2026 revision) with its four domains and weights: prepare the data (25–30%), model the data (25–30%), visualize and analyze the data (25–30%), and manage and secure Power BI (15–20%). It covers what the 2026 updates added: Direct Lake, calculation groups, visual calculations, DAX query view and Copilot in Power BI. The spine of the course is the shift from SQL thinking to DAX thinking: filter context, row context, CALCULATE and context transition, taught with small models you can compute in your head before you check them in Power BI Desktop. Every topic follows the same order: the pain, the solution, and what it costs — memory, refresh time, query latency, licences or security risk. Labs run in Power BI Desktop (free) on a synthetic Aldervane Advisory LLP dataset — timesheets, invoices with three role-playing dates, month-end WIP balances and a nested JSON export — published on GitHub. Service features (workspaces, apps, RLS, refresh) are shown step by step, with a free route to a sandbox tenant and a read-the-screenshots path for learners whose corporate tenant blocks publishing. Practice uses the formats of the real exam: single and multiple choice, matching, Yes/No statement sets, "what does this measure return in this cell" and DAX completion. Isotype-style pictures summarise one idea each. Optional units cover PBIP/TMDL and Git, a SQL-to-DAX phrasebook, client-confidentiality patterns and the bridge to Microsoft Fabric.

Path through the course

· 28 units

the order is free · the steps below are the order the author suggests, not a conditionView: map · steps

4 · Domain 1 · Prepare the data

Profile and clean the data

~2 h

by the author’s design, after “Connect to data: sources, shared semantic models, credentials and parameters”

9 · Domain 2 · Model the data

A common date table

~1.5 h

by the author’s design, after “Star schema: facts, dimensions and table properties”

13 · Domain 2 · Model the data

Calculation groups

~2 h

by the author’s design, after “DAX III: time intelligence, semi-additive measures, statistics and quick measures”

14 · Domain 2 · Model the data

Optimize model performance

~2 h

by the author’s design, after “DAX I: measures, calculated columns and evaluation context”

16 · Domain 3 · Visualize and analyze data

Visual calculations

~1.5 h

by the author’s design, after “DAX II: CALCULATE and context transition”

17 · Domain 3 · Visualize and analyze data

Copilot in Power BI

~2 h

by the author’s design, after “Build the report: visuals, formatting, themes, filters and paginated reports”

22 · Domain 4 · Manage and secure Power BI

Gateways and scheduled refresh

~2 h

by the author’s design, after “Workspaces, apps, publishing and distribution”

26 · Optional · For developers

SQL to DAX phrasebook

~2 h

by the author’s design, after “DAX II: CALCULATE and context transition”

final · in the browser, without the assistant

Exam

closed · enrol on the course

The path in words
  • Stage 1 · Exam map and the Power BI landscape — ~1.5 h
  • Stage 2 · Connect to data: sources, shared semantic models, credentials and parameters — by the author’s design, after “Exam map and the Power BI landscape”; ~2 h
  • Stage 2 · Storage modes: Import, DirectQuery and Direct Lake — by the author’s design, after “Exam map and the Power BI landscape”; ~2 h
  • Stage 2 · Workspaces, apps, publishing and distribution — by the author’s design, after “Exam map and the Power BI landscape”; ~2 h
  • Stage 3 · Profile and clean the data — by the author’s design, after “Connect to data: sources, shared semantic models, credentials and parameters”; ~2 h
  • Stage 3 · Gateways and scheduled refresh — by the author’s design, after “Workspaces, apps, publishing and distribution”; ~2 h
  • Stage 3 · Security and governance: roles, item access, RLS and sensitivity labels — by the author’s design, after “Workspaces, apps, publishing and distribution”; ~2.5 h
  • Stage 3 · Bridge to Microsoft Fabric: lakehouse, Direct Lake and the road to DP-600 — by the author’s design, after “Storage modes: Import, DirectQuery and Direct Lake”; optional; ~2 h
  • Stage 4 · Transform I: types, columns, grouping, pivoting and JSON — by the author’s design, after “Profile and clean the data”; ~2.5 h
  • Stage 4 · Exam rehearsal: a case study, time management and traps — by the author’s design, after “Security and governance: roles, item access, RLS and sensitivity labels”; ~3 h
  • Stage 4 · Aldervane: client confidentiality in Power BI — by the author’s design, after “Security and governance: roles, item access, RLS and sensitivity labels”; optional; ~2 h
  • Stage 5 · Transform II: merge, append, reference vs duplicate, keys and load settings — by the author’s design, after “Transform I: types, columns, grouping, pivoting and JSON”; ~2 h
  • Stage 6 · Star schema: facts, dimensions and table properties — by the author’s design, after “Transform II: merge, append, reference vs duplicate, keys and load settings”; ~2 h
  • Stage 7 · Relationships: cardinality, cross-filter direction and role-playing dimensions — by the author’s design, after “Star schema: facts, dimensions and table properties”; ~2.5 h
  • Stage 7 · A common date table — by the author’s design, after “Star schema: facts, dimensions and table properties”; ~1.5 h
  • Stage 7 · DAX I: measures, calculated columns and evaluation context — by the author’s design, after “Star schema: facts, dimensions and table properties”; ~2 h
  • Stage 7 · Build the report: visuals, formatting, themes, filters and paginated reports — by the author’s design, after “Star schema: facts, dimensions and table properties”; ~2 h
  • Stage 7 · Developer mode: PBIP, TMDL, Git integration and deployment pipelines — by the author’s design, after “Star schema: facts, dimensions and table properties”; optional; ~2 h
  • Stage 8 · DAX II: CALCULATE and context transition — by the author’s design, after “DAX I: measures, calculated columns and evaluation context”; ~2.5 h
  • Stage 8 · Optimize model performance — by the author’s design, after “DAX I: measures, calculated columns and evaluation context”; ~2 h
  • Stage 8 · Copilot in Power BI — by the author’s design, after “Build the report: visuals, formatting, themes, filters and paginated reports”; ~2 h
  • Stage 8 · Storytelling: bookmarks, tooltips, interactions, navigation and drillthrough — by the author’s design, after “Build the report: visuals, formatting, themes, filters and paginated reports”; ~2.5 h
  • Stage 8 · Analyze: patterns, trends, outliers and AI visuals — by the author’s design, after “Build the report: visuals, formatting, themes, filters and paginated reports”; ~2.5 h
  • Stage 9 · DAX III: time intelligence, semi-additive measures, statistics and quick measures — by the author’s design, after “A common date table”, “DAX II: CALCULATE and context transition”; ~2.5 h
  • Stage 9 · Visual calculations — by the author’s design, after “DAX II: CALCULATE and context transition”; ~1.5 h
  • Stage 9 · Usability: mobile, personalization, accessibility, export and page refresh — by the author’s design, after “Storytelling: bookmarks, tooltips, interactions, navigation and drillthrough”; ~2 h
  • Stage 9 · SQL to DAX phrasebook — by the author’s design, after “DAX II: CALCULATE and context transition”; optional; ~2 h
  • Stage 10 · Calculation groups — by the author’s design, after “DAX III: time intelligence, semi-additive measures, statistics and quick measures”; ~2 h

Programme · 28 units

1Exam map and the Power BI landscape

Describe the PL-300 exam: four domains and their weights, time, pass mark, question formats, case studies and Microsoft Learn access during the exam

~1.5 h
2Connect to data: sources, shared semantic models, credentials and parameters

Connect to files, databases and the Aldervane CSV/JSON files, and recognise the M function each connector writes into the Source step

~2 h
3Storage modes: Import, DirectQuery and Direct Lake

Explain where the data is at query time in Import, DirectQuery and Direct Lake, and how each one gets fresh data (refresh, live queries, framing)

~2 h
4Profile and clean the data

Use column quality, column distribution and column profile to evaluate data, and switch profiling from the top 1 000 rows to the entire data set

~2 h
5Transform I: types, columns, grouping, pivoting and JSON

Select appropriate data types in Power Query, including Fixed decimal number for money, Text for codes, and a correct conversion of yyyymmdd numbers to dates

~2.5 h
6Transform II: merge, append, reference vs duplicate, keys and load settings

Choose the right Power Query join kind (left/right/full outer, inner, left/right anti) for a requirement and predict the row count of a merge

~2 h
7Star schema: facts, dimensions and table properties

Classify tables as facts or dimensions, state each fact table's grain, and recognise transaction, periodic snapshot and factless (bridge) facts

~2 h
8Relationships: cardinality, cross-filter direction and role-playing dimensions

Choose the cardinality of a relationship (one-to-many, one-to-one, many-to-many) from the uniqueness of the columns and the grain of the tables

~2.5 h

Show the remaining 20 units