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Prepare the dataeasyMultiple ChoiceObjective-mapped

PL-300 Prepare the data Practice Question

You have a Power BI dataset that uses Import mode and refreshes daily. The source data includes a column 'LastModifiedDate'. You want to reduce the amount of data loaded during each refresh by only loading rows that have changed since the last refresh. Which feature should you configure?

⚠ Common exam trap

Test-takers frequently confuse incremental refresh with query folding or 'Reduce data' options, not realizing that incremental refresh is the only feature designed to load only changed rows in Import mode while keeping the dataset in Import mode.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Configure incremental refresh on the table using the 'LastModifiedDate' column.

Incremental refresh in Power BI allows you to filter data by a date/time column (such as 'LastModifiedDate') so that only rows that have changed since the last refresh are loaded. This reduces refresh time and data volume while still using Import mode. The feature requires a date/time column and a properly configured policy in the Power Query Editor.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Enable query folding in Power Query.

    Why it's wrong here

    Query folding pushes transformations back to the source database (e.g., SQL Server) so that only necessary rows and columns are pulled into Power Query. While this reduces initial data transfer and speeds up refresh, it does not address the core issue of re-importing an entire large table on every refresh; incremental refresh partitions the table by a date/time range. Without incremental refresh, query folding still imports all unchanged rows each cycle, so it is not a refresh-time reduction strategy for full table reloads.

  • Use the 'Reduce data' option in Power Query Editor.

    Why it's wrong here

    The Reduce data option (a preview feature) lets you toggle a row limit in the Power Query Editor to make data previews fast during query development, but it has no effect on the actual dataset refresh after publishing. It does not create any incremental loading mechanism or filter existing rows; it only affects the editor's preview window. Scheduled refresh in the service will still load the entire table, so this option provides no benefit to production refresh time.

  • Change the storage mode to DirectQuery.

    Why it's wrong here

    With DirectQuery, Power BI does not import data into memory; instead, every visual query translates to source queries in real time, eliminating the notion of a scheduled 'refresh' that loads data. However, DirectQuery fundamentally changes the interactive query performance and places additional load on the source system; it also sacrifices many Power Query transformations, and you lose the speed of in-memory analytics. Moreover, it does not 'reduce refresh time' because there is no scheduled data import—it's a different architecture, not an optimization of the existing import-mode refresh process.

  • Configure incremental refresh on the table using the 'LastModifiedDate' column.

    Why this is correct

    Incremental refresh creates named date/time range partitions on a table and, during refresh, loads only new or modified partitions (e.g., the last N days) rather than the entire table. By using a 'LastModifiedDate' column, the refresh engine can detect which rows have changed and only refresh those historical partitions, dramatically reducing refresh time and data transfer. This requires using a Refresh Policy in Power Query (e.g., RangeStart and RangeEnd parameters) and the table must have a date/time column that records when each row was last modified.

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