PL-300 Prepare the data Practice Question
You are connecting Power BI to a SQL Server database. The database contains a table with millions of sales transactions. You need to design a data model that minimizes load time and memory usage while still allowing analysis of sales by date, product, and customer. Which modeling approach should you use?
⚠ Common exam trap
The trap is that candidates often choose DirectQuery (Option B) thinking it saves memory and load time because it doesn't import data. However, this overlooks that Import mode with a star schema provides better query performance and memory efficiency through compression for large datasets, while DirectQuery increases query latency and source system load.
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
✓
Import the data into Power BI, creating a star schema with date, product, and customer dimension tables and the sales fact table.
Importing the data into Power BI and modeling it as a star schema minimizes query load time (the time to run interactive analyses) and memory usage through columnar compression and optimized query performance. Import mode stores data in the VertiPaq engine, which compresses data efficiently, especially with a star schema, reducing memory footprint relative to flattened tables and enabling fast in-memory analysis. While DirectQuery does not store data in Power BI (thus lower memory usage and no data refresh load time), it results in slower query performance and higher load on the source database, which is not ideal for analyzing millions of rows interactively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a composite model with some tables in DirectQuery and others in Import.
Why it's wrong here
A composite model that mixes DirectQuery and Import tables introduces unnecessary storage-mode complexity and can create query performance ambiguity when relationships span both modes. It also forces you to manage query reduction techniques just to keep the DirectQuery side performant. For a single SQL Server source with a typical analytical workload, this hybrid approach adds maintenance overhead without solving a real problem.
- ✗
Use DirectQuery mode to avoid storing data in Power BI.
Why it's wrong here
Pure DirectQuery lets you avoid taking a data copy in Power BI, but it also means every visual interaction is round-tripped to SQL Server, which often causes noticeable latency on large fact tables or dense dashboards. You also lose in-memory compression and columnstore acceleration, so aggregations can be slow. For high-volume analytical queries, this is usually the wrong trade-off.
- ✓
Import the data into Power BI, creating a star schema with date, product, and customer dimension tables and the sales fact table.
Why this is correct
Import mode is the right choice here because you get Power BI's in-memory columnstore engine, which compresses the data and makes slicers and filters nearly instantaneous. Building a proper star schema with separate date, product, and customer dimensions plus a sales fact table gives you clean one-to-many relationships and lets you write efficient DAX measures. This design minimizes memory usage while maximizing query performance.
- ✗
Use a live connection to an existing SQL Server Analysis Services tabular model.
Why it's wrong here
A live connection to an SSAS tabular model requires that the model already exists and is maintained, so it's not a viable option unless you're prepared to build and manage a separate Analysis Services layer. Even if an SSAS instance exists, you're then constrained by its performance and governance policies rather than controlling the data flow directly in Power BI. Here, pointing Power BI at the SQL Server and importing into a star schema is far simpler.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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