PL-300 Model the data Practice Question

Answer analysis

Option-by-option breakdown

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

• ✗

  Disable the auto-date/time feature.

  Why it's wrong here

  Disabling the auto-date/time feature in Power BI removes the automatic date hierarchies that Power BI creates for each date column, which reduces model size and can shorten refresh times by eliminating hidden tables. However, this option only trims metadata and does not alter the granularity of the underlying fact table, so large transaction-level tables still require full scans during report rendering. Without a pre-aggregated summary table, report queries still process millions of rows, and the performance bottleneck remains unresolved.

• ✗

  Increase the data load frequency to every 15 minutes.

  Why it's wrong here

  Increasing the data load frequency to every 15 minutes ensures the dataset is refreshed more often, which can improve data freshness but has no impact on the computational cost of querying a large fact table. Each refresh operation consumes system resources and may even slow down the underlying source database due to repeated extraction, while report queries continue to aggregate the full transactional granularity on the fly. Query performance in Power BI is determined by the structure and size of the data model, not by how recently the data was loaded, so this change does not address the need for a pre-aggregated sales table at the month and product category level.

• ✗

  Use DirectQuery mode to query the source database directly.

  Why it's wrong here

  Using DirectQuery mode to query the Azure SQL database directly sends every report visual's DAX query to the source system as T-SQL, which means report performance is entirely dependent on the database's indexing, concurrency, and network latency. Without an aggregation layer, DirectQuery must aggregate potentially millions of sales rows in real time for each filter or slicer interaction, often causing slower response times than an imported, in-memory model that uses efficient columnar compression. Moreover, DirectQuery bypasses Power BI's ability to cache aggregated results, so even if the source is optimized, it cannot match the performance of a dedicated aggregate table designed for the report's common grain.

• ✓

  Create an aggregate table in Power BI that pre-aggregates sales by month and product category.

  Why this is correct

  Creating an aggregate table in Power BI that pre-aggregates sales by month and product category is the correct approach because it reduces the fact table to a much coarser grain, shrinking the number of rows that report queries must scan. By configuring this aggregate table as an aggregation group in the model, Power BI can automatically route high-level visual queries to the small summary table while reserving the detailed fact table for drill-down operations. This leverages the storage engine's in-memory columnar compression and accelerates time-intelligence calculations such as year-over-year month comparisons, directly addressing the performance bottleneck caused by large transaction-level data.

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