PL-300 Prepare the data Practice Question
Your organization uses Power BI to analyze sales data stored in Azure SQL Database. The data model includes a fact table with millions of rows. To improve performance, you need to reduce the amount of data loaded into the model. Which action should you take?
⚠ Common exam trap
Many exam-takers confuse incremental refresh with reducing the initial data load, but incremental refresh only optimizes refresh cycles over time and does not limit the first full load unless combined with a date filter in Power Query.
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
✓
Apply row-level filters in Power Query to import only relevant rows
Applying row-level filters in Power Query reduces the volume of data imported into the Power BI model by only loading rows that meet specific criteria. This directly minimizes the data footprint in memory, improving query and refresh performance, especially for fact tables with millions of rows stored in Azure SQL Database.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply row-level filters in Power Query to import only relevant rows
Why this is correct
Row-level filtering in Power Query (M) is applied before data enters the VertiPaq engine, enabling you to import only the rows that are relevant to your analysis. This reduces the number of rows materialized in the model, which directly shrinks the overall memory footprint and improves refresh time, query performance, and columnar compression ratios. Because only the necessary data is loaded, all downstream measures and reports run over a targeted dataset, making this the most effective way to reduce the initial data load size.
- ✗
Use calculated tables in DAX to summarize data
Why it's wrong here
Calculated tables in DAX are defined after the data has already been loaded into the model, so they cannot influence the amount of data imported from the source. Once created, calculated tables are materialized as new tables in memory, adding to the model's size rather than reducing it. While they can improve query performance by pre-aggregating or denormalizing data, they consume additional memory and storage, which contradicts the goal of minimizing the initial load.
- ✗
Disable the Auto Date/Time feature
Why it's wrong here
Disabling the Auto Date/Time feature removes the automatic creation of hidden date tables and date hierarchies for every date column in the model. This reduces the model's metadata size and memory footprint, but it does not change how many rows or columns are loaded from the source. The data is still fully imported and stored in VertiPaq before the feature is turned off, so this setting is a model-size optimization, not a technique for reducing the volume of data loaded from the data source.
- ✗
Configure incremental refresh with a date filter
Why it's wrong here
Incremental refresh with a date filter partitions a table and schedules refreshes to only process new or changed partitions, which reduces ongoing refresh time and data transfer. However, the initial load of the entire table still occurs on the first publish or import, so it does not reduce the amount of data loaded at that point. Moreover, incremental refresh requires a date filter and a properly configured range, and without that filter it cannot function at all, making it incorrect for reducing the initial data load size.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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