PL-300 Model the data Practice Question
You are creating a Power BI report that uses a composite model (DirectQuery for large tables and Import for small dimension tables). You want to ensure that measures referencing the DirectQuery tables are responsive. Which of the following design choices should you avoid?
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
✓
Use complex time intelligence measures that iterate over the fact table
The design choice to avoid is A: complex time intelligence measures that iterate over the fact table, because in a composite model the DirectQuery fact table is queried remotely, and row-by-row iteration (e.g., SUMX over the fact table) forces expensive, non-foldable queries that hurt responsiveness. Measures that aggregate columns (B) can often be folded into a single SQL GROUP BY, so they are efficient. Importing a date dimension (C) is a recommended pattern because it keeps time intelligence calculations local and foldable. User-defined aggregations (D) are also recommended, as they cache pre-aggregated DirectQuery data and improve query performance.
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 complex time intelligence measures that iterate over the fact table
Why this is correct
Correct. In a composite model, the large fact table typically remains in DirectQuery to avoid memory pressure, but measures that iterate row-by-row (e.g., SUMX, or time-intelligence functions that scan a period) will force the DAX engine to issue multiple or very broad SQL queries against the remote source. Each iteration may expand into a separate query or pull large result sets, causing severe latency and poor report responsiveness. Time intelligence that needs to re-evaluate the fact table for each date context is especially costly and should be avoided in DirectQuery-heavy models.
- ✗
Use measures that aggregate columns rather than rows
Why it's wrong here
Incorrect. Aggregating a column (for example, SUM(Sales[Amount]) or MIN(Sales[Date])) is actually a best practice for DirectQuery because the entire aggregation is pushed down to the source database as a single SQL aggregate query, returning only a scalar result to Power BI. This avoids transferring or materializing large volumes of raw rows across the network. Because this is an efficient technique, it is not the performance problem the question is asking you to identify.
- ✗
Create a date dimension table imported from the source
Why it's wrong here
Incorrect. Importing a small date dimension table into the composite model is not only harmless but often recommended as a performance optimization. It reduces query round-trips, enables faster time-intelligence calculations, and lets DAX functions like TOTALYTD or SAMEPERIODLASTYEAR work against a local, indexed copy of dates. The real issue in this scenario is leaving the large fact table in DirectQuery and then forcing it through row-level iteration, not using an imported lookup table.
- ✗
Create user-defined aggregations for the DirectQuery table
Why it's wrong here
Incorrect. User-defined aggregations are an explicit performance feature in Power BI that pre-calculate stored copies of aggregated data (e.g., at a month or category level) so that queries can avoid hitting the full DirectQuery table when possible. This is a well-known optimization technique for large fact tables and is not a mistake. This option is therefore a valid design choice rather than the forbidden slow approach described in the question.
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