1 · Start
Exam map and the Power BI landscape
~1,5 Std.

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.
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Ihr Assistent holt das Kursmaterial von unserem Server und leitet die Lektion in Ihrer Sprache.
Die Prüfung wird auf der Website abgelegt, ohne Assistenten. Prüfungsfragen werden nicht übersetzt: Das ist für jeden Kurs so festgelegt.
freie Reihenfolge · die Stufen unten sind die vom Autor vorgeschlagene Reihenfolge, keine BedingungAnsicht: Karte · Stufen
1 · Start
Exam map and the Power BI landscape
~1,5 Std.
2 · Domain 1 · Prepare the data
Connect to data: sources, shared semantic models, credentials and parameters
~2 Std.
laut Plan des Autors nach „Exam map and the Power BI landscape“
3 · Domain 1 · Prepare the data
Storage modes: Import, DirectQuery and Direct Lake
~2 Std.
laut Plan des Autors nach „Exam map and the Power BI landscape“
4 · Domain 1 · Prepare the data
~2 Std.
laut Plan des Autors nach „Connect to data: sources, shared semantic models, credentials and parameters“
5 · Domain 1 · Prepare the data
Transform I: types, columns, grouping, pivoting and JSON
~2,5 Std.
laut Plan des Autors nach „Profile and clean the data“
6 · Domain 1 · Prepare the data
Transform II: merge, append, reference vs duplicate, keys and load settings
~2 Std.
laut Plan des Autors nach „Transform I: types, columns, grouping, pivoting and JSON“
7 · Domain 2 · Model the data
Star schema: facts, dimensions and table properties
~2 Std.
laut Plan des Autors nach „Transform II: merge, append, reference vs duplicate, keys and load settings“
8 · Domain 2 · Model the data
Relationships: cardinality, cross-filter direction and role-playing dimensions
~2,5 Std.
laut Plan des Autors nach „Star schema: facts, dimensions and table properties“
9 · Domain 2 · Model the data
~1,5 Std.
laut Plan des Autors nach „Star schema: facts, dimensions and table properties“
10 · Domain 2 · Model the data
DAX I: measures, calculated columns and evaluation context
~2 Std.
laut Plan des Autors nach „Star schema: facts, dimensions and table properties“
11 · Domain 2 · Model the data
DAX II: CALCULATE and context transition
~2,5 Std.
laut Plan des Autors nach „DAX I: measures, calculated columns and evaluation context“
12 · Domain 2 · Model the data
DAX III: time intelligence, semi-additive measures, statistics and quick measures
~2,5 Std.
laut Plan des Autors nach „A common date table“, „DAX II: CALCULATE and context transition“
13 · Domain 2 · Model the data
~2 Std.
laut Plan des Autors nach „DAX III: time intelligence, semi-additive measures, statistics and quick measures“
14 · Domain 2 · Model the data
~2 Std.
laut Plan des Autors nach „DAX I: measures, calculated columns and evaluation context“
15 · Domain 3 · Visualize and analyze data
Build the report: visuals, formatting, themes, filters and paginated reports
~2 Std.
laut Plan des Autors nach „Star schema: facts, dimensions and table properties“
16 · Domain 3 · Visualize and analyze data
~1,5 Std.
laut Plan des Autors nach „DAX II: CALCULATE and context transition“
17 · Domain 3 · Visualize and analyze data
~2 Std.
laut Plan des Autors nach „Build the report: visuals, formatting, themes, filters and paginated reports“
18 · Domain 3 · Visualize and analyze data
Storytelling: bookmarks, tooltips, interactions, navigation and drillthrough
~2,5 Std.
laut Plan des Autors nach „Build the report: visuals, formatting, themes, filters and paginated reports“
19 · Domain 3 · Visualize and analyze data
Usability: mobile, personalization, accessibility, export and page refresh
~2 Std.
laut Plan des Autors nach „Storytelling: bookmarks, tooltips, interactions, navigation and drillthrough“
20 · Domain 3 · Visualize and analyze data
Analyze: patterns, trends, outliers and AI visuals
~2,5 Std.
laut Plan des Autors nach „Build the report: visuals, formatting, themes, filters and paginated reports“
21 · Domain 4 · Manage and secure Power BI
Workspaces, apps, publishing and distribution
~2 Std.
laut Plan des Autors nach „Exam map and the Power BI landscape“
22 · Domain 4 · Manage and secure Power BI
Gateways and scheduled refresh
~2 Std.
laut Plan des Autors nach „Workspaces, apps, publishing and distribution“
23 · Domain 4 · Manage and secure Power BI
Security and governance: roles, item access, RLS and sensitivity labels
~2,5 Std.
laut Plan des Autors nach „Workspaces, apps, publishing and distribution“
24 · Exam rehearsal
Exam rehearsal: a case study, time management and traps
~3 Std.
laut Plan des Autors nach „Security and governance: roles, item access, RLS and sensitivity labels“
25 · Optional · For developers
Developer mode: PBIP, TMDL, Git integration and deployment pipelines
~2 Std.
laut Plan des Autors nach „Star schema: facts, dimensions and table properties“
26 · Optional · For developers
~2 Std.
laut Plan des Autors nach „DAX II: CALCULATE and context transition“
27 · Optional · Aldervane
Aldervane: client confidentiality in Power BI
~2 Std.
laut Plan des Autors nach „Security and governance: roles, item access, RLS and sensitivity labels“
28 · Optional · Fabric
Bridge to Microsoft Fabric: lakehouse, Direct Lake and the road to DP-600
~2 Std.
laut Plan des Autors nach „Storage modes: Import, DirectQuery and Direct Lake“
Abschluss · im Browser, ohne den Assistenten
Prüfung
gesperrt · in den Kurs einschreiben
| 1 | Exam 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 Std. |
| 2 | Connect 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 Std. |
| 3 | Storage 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 Std. |
| 4 | Profile 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 Std. |
| 5 | Transform 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 Std. |
| 6 | Transform 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 Std. |
| 7 | Star 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 Std. |
| 8 | Relationships: 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 Std. |