PL-300 Model the data Practice Question
A company has a Power BI semantic model that uses DirectQuery to a SQL Server database. The model contains a large fact table with sales data. Users report that reports using this model are slow. Which design change would most improve query performance?
⚠ Common exam trap
Watch out — candidates often assume switching to Import mode is always the best performance fix, but the question specifically asks for a design change that improves query performance in DirectQuery mode, where reducing column count is a more targeted and less disruptive solution.
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
✓
Remove unnecessary columns from the fact table.
Removing unnecessary columns from the fact table reduces the amount of data that must be transferred from SQL Server to Power BI for each query. In DirectQuery mode, every report interaction sends a query to the source database, so fewer columns mean smaller result sets and faster query execution. This directly addresses the performance bottleneck caused by a large fact table without changing the underlying storage mode.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all relationships between tables.
Why it's wrong here
Removing all relationships between tables is incorrect because DirectQuery models depend on relationships to translate filter context into JOIN conditions in the native SQL queries sent to the source. Without them, visuals cannot correctly sift related data, often producing inflated results from Cartesian products or blank values, and the model loses its semantic integrity. This would break reporting rather than improve performance, so it is not a valid tuning step.
- ✗
Switch the model to Import mode.
Why it's wrong here
Switching the model to Import mode would change the storage engine and eliminate the direct querying overhead, but it abandons the primary reason DirectQuery was used: to handle massive data volumes or source data that must remain live. Importing the entire fact table could exceed available memory, require lengthy refresh cycles, and break real-time freshness requirements. In many enterprise scenarios this is simply not feasible, so it is not an acceptable optimization for a DirectQuery model.
- ✓
Remove unnecessary columns from the fact table.
Why this is correct
Removing unnecessary columns from the fact table is correct because DirectQuery operates by pushing queries back to the source, and every extraneous column widens the SELECT statement, increasing network transfer and source-side processing. A narrower fact table means fewer columns are scanned and materialized for each visual interaction, which directly reduces query latency and memory overhead. This is a standard column-pruning practice for DirectQuery performance tuning.
- ✗
Disable the 'Reduce queries' option in report settings.
Why it's wrong here
Disabling the 'Reduce queries' option is wrong because that setting is designed to limit the number of underlying data queries triggered by visual interactions, such as disabling cross-highlighting or cross-filtering. Turning it off will cause more queries to be generated as users click and interact with the report, increasing the load on the DirectQuery source and worsening performance. It should be kept enabled or configured to reduce unnecessary queries rather than disabled.
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