Power BI Data Analyst (PL-300)

Key Topics to Master:

• Identifying and connecting to data sources or a shared semantic model
• Changing data source settings, including credentials and privacy levels
• Choosing between DirectLake, DirectQuery, and Import
• Creating and modifying parameters
• Evaluating data with data statistics and column properties, and resolving nulls and quality issues
• Selecting column data types, creating and transforming columns, grouping and aggregating rows
• Pivot, unpivot, and transpose, and converting semi-structured data to a table
• Reference against duplicate queries and the impact of each
• Merge and append queries, creating keys for relationships, and configuring data loading for queries

Key Topics to Master:

• Configuring table and column properties and implementing role-playing dimensions
• Defining relationship cardinality and cross-filter direction
• Creating a common date table
• Use cases for calculated columns against calculated tables
• Single aggregation measures and the CALCULATE function
• Time intelligence measures, basic statistical functions, and semi-additive measures
• Quick measures and calculation groups
• Identifying poorly performing measures, relationships, and visuals with Performance Analyzer and DAX query view
• Improving performance by removing unnecessary rows and columns and by reducing granularity

Key Topics to Master:

• Selecting, formatting, and configuring visuals, themes, and conditional formatting
• Slicing, filtering, sync slicers, and editing interactions between visuals
• Creating a narrative visual with Copilot, and using Copilot to create or suggest content for a report page
• Choosing when to use a paginated report, and creating visual calculations with DAX
• Bookmarks, custom tooltips, report navigation, sorting, and the Selection pane
• Drillthrough navigation including pages, filters, and buttons
• Export settings, mobile layouts, personalization and personalized visuals, and accessibility design
• Configuring automatic page refresh
• The Analyze feature, grouping, binning, clustering, AI visuals, reference lines, error bars, forecasting, and anomaly detection

Key Topics to Master:

• Creating and configuring a workspace, and configuring and updating an app
• Publishing, importing, or updating items in a workspace, and creating dashboards
• Choosing a distribution method
• Configuring subscriptions and data alerts
• Promoting or certifying Power BI content
• Identifying when a gateway is required
• Configuring a semantic model scheduled refresh
• Assigning workspace roles, configuring item-level access, and configuring access to semantic models
• Implementing row-level security roles, configuring row-level security group membership, and applying sensitivity labels

• 1Read the PL-300 study guide on Microsoft Learn and note that the skills measured are dated April 20, 2026, then check that any course you buy matches that date.
• 2Install Power BI Desktop and connect to a folder of CSVs, a SQL source, and a shared semantic model so you have seen all three connection paths.
• 3Change data source settings, including credentials and privacy levels, and note the error Power Query raises when privacy levels block a merge.
• 4Create two parameters and use them to switch a source path without editing the query.
• 5Visit the Microsoft exam sandbox to see the interface and the question types before you spend any time on content.

• 1Build the same report three times over Import, DirectQuery, and DirectLake and write down the trade-off in refresh, latency, and available DAX for each.
• 2Turn on column distribution, column quality, and column profile in Power Query and use them to find nulls and outliers in a real dataset.
• 3Break an import on purpose with a bad data type and practise resolving the import error rather than deleting the row.
• 4Work through the Microsoft Learn module on getting data in Power BI end to end.

• 1Practise pivot, unpivot, and transpose on a wide spreadsheet until you can predict the shape before you click.
• 2Convert a nested JSON column into a table and expand it into columns.
• 3Build fact and dimension tables from one flat file, including creating a surrogate key for the relationship.
• 4Use reference and duplicate on the same query and confirm what changes about refresh behaviour and the dependency chain.
• 5Merge and append queries, and turn off Enable load on a staging query to keep it out of the model.

• 1Build a star schema and set cardinality and cross-filter direction on each relationship deliberately rather than accepting the defaults.
• 2Implement a role-playing dimension with an order date and a ship date, using both an inactive relationship with USERELATIONSHIP and a duplicated date table, and compare the two.
• 3Create a common date table with DAX and mark it as a date table.
• 4Hide key columns, set summarization to none on numeric keys, and set sort-by columns for month names.
• 5Write down three cases where a calculated column is right and three where a measure is right.

• 1Write aggregation measures with SUM, AVERAGE, COUNTROWS, and DISTINCTCOUNT and confirm each against a matrix visual.
• 2Use CALCULATE with filter arguments, with ALL, and with REMOVEFILTERS, and explain the result each time before you check it.
• 3Work the Microsoft Learn learning path on creating model calculations with DAX in Power BI.
• 4Build a quick measure, then rewrite the generated DAX by hand to understand what it did.
• 5Create a semi-additive measure such as closing balance and confirm it does not sum across dates.

• 1Write year to date, prior year, and year over year growth measures with the time intelligence functions and test them at month, quarter, and year grain.
• 2Install Tabular Editor and create a calculation group that applies those time calculations to any measure.
• 3Confirm what happens to a calculation group when the date table is not marked as a date table.
• 4Create a calculated table with SUMMARIZE or a filtered copy and note when a calculated table beats a Power Query table.

• 1Run Performance Analyzer on a slow page and read the DAX query, visual display, and other timings separately.
• 2Copy a slow query out of Performance Analyzer into DAX query view and iterate on it there.
• 3Remove unused columns and reduce a datetime column to a date, then compare the file size before and after.
• 4Reduce granularity by pre-aggregating a fact table in Power Query and measure the difference.
• 5Read the Microsoft Learn guidance on optimizing a model for performance.

• 1Rebuild one report page using five different visual types for the same measure and write down which question each answers best.
• 2Apply a custom theme JSON file and confirm it survives adding new visuals.
• 3Apply conditional formatting by rules, by field value, and by colour scale.
• 4Create a visual calculation with DAX on a matrix and compare it against an equivalent measure.
• 5Build one paginated report in Power BI Report Builder so you know when to choose it over a Power BI report.

• 1Build bookmarks with the Selection pane to swap two visuals in the same space, and add buttons for navigation.
• 2Configure drillthrough with a page filter and a back button, then add a custom tooltip page.
• 3Set up sync slicers across three pages and edit interactions so one visual does not filter another.
• 4Use Copilot to create a narrative visual, to create a report page, and to summarize the semantic model, since all three appear as separate objectives.
• 5Design one page for mobile, enable personalization, and run the accessibility checks Microsoft documents.

• 1Use the Analyze feature to explain an increase, then use grouping, binning, and clustering on the same dataset.
• 2Add a forecast, a reference line, and error bars, and detect anomalies on a time series.
• 3Publish to a workspace, configure and update an app, and choose between app, direct share, and workspace access as distribution methods.
• 4Configure a scheduled refresh, then break it by pointing at an on-premises source so you can see when a gateway is required.
• 5Set up a subscription and a data alert on a dashboard tile.

• 1Assign all four workspace roles to test users and record exactly what each can do.
• 2Create static and dynamic row-level security roles, test them with View as, and then configure group membership for a role in the service.
• 3Apply sensitivity labels and promote and certify a semantic model.
• 4Take the free Microsoft Practice Assessment for PL-300 and record your score by skill area.
• 5Sit a full practice run under a strict 100-minute clock, with only learn.microsoft.com open, to rehearse the real resource conditions.

• 1Re-read the four skills-measured lists and mark every bullet you cannot demonstrate in Power BI Desktop without help.
• 2Rehearse the Manage and secure Power BI objectives, which are 15 to 20 percent and the easiest area to leave untouched if you only work in Desktop.
• 3Book through Pearson VUE, remembering you can schedule no more than 90 days ahead and hold at most two Microsoft bookings at once.
• 4Practise using the Microsoft Learn split screen for two lookups only, so the habit is bounded before exam day.
• 5Keep the next day free, since a first failure allows a retake after only 24 hours.