PL-300 Visualize and analyze the data Practice Question
You are building a Power BI report that uses a composite model with a DirectQuery source and an imported local table. You want to create a measure that aggregates data from both sources. What must you ensure?
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
✓
The relationships between tables must be defined correctly.
The correct answer is A: relationships between tables must be defined correctly. In a Power BI composite model, a measure can aggregate data from both a DirectQuery source and an imported local table only when the tables are related through properly defined relationships, which allows the DAX engine to propagate filters across storage-mode boundaries. Without a valid relationship (correct cardinality and cross-filter direction), the measure cannot combine the two sources. Option B is false because composite models explicitly support measures spanning Import and DirectQuery tables. Option C is wrong because composite models are designed to mix storage modes, not force them to match. Option D is unnecessary and would defeat the purpose of using an imported local table.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The relationships between tables must be defined correctly.
Why this is correct
In a composite model, relationships between tables from different storage modes are the only way Power BI can join and aggregate data across sources at query time. A correctly defined relationship—with proper cardinality and filter direction—ensures that cross-source measures, such as a SUM over an imported table filtered by a DirectQuery table, produce accurate results. Without those relationships, the engine cannot determine how to combine rows, causing errors, blank values, or meaningless totals.
- ✗
You cannot create measures that use columns from both sources.
Why it's wrong here
This is false because composite models explicitly support measures that reference columns from both imported and DirectQuery tables. When a measure combines columns from two sources, Power BI evaluates the portions separately using the relationships to align rows, then merges the partial results. For instance, a calculated ratio could divide a metric from a DirectQuery sales table by a metric from an imported budget table without converting either table.
- ✗
Both tables must be set to the same storage mode (DirectQuery or Import).
Why it's wrong here
Composite models are specifically designed to allow each table to have its own storage mode—some imported, some DirectQuery—within the same model. Forcing both tables to the same mode would negate the benefit of mixing fast in-memory aggregation with real-time direct access. Moreover, storage mode is a property set per table, not a global model setting, so heterogeneous configurations are both supported and expected.
- ✗
The local table must be converted to DirectQuery.
Why it's wrong here
A local table (such as one created via Enter Data or a calculated table) remains in Import storage mode and can coexist with a DirectQuery table without issue. In fact, converting it to DirectQuery is usually impossible or nonsensical because it has no underlying database source. The recommended pattern is to keep the imported local table as a dimension table that filters the DirectQuery fact table; the relationships handle the joining.
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