PL-300 Model the data Practice Question
Which THREE of the following are valid reasons to use a composite model (mixed storage mode) in Power BI?
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
✓
To enable real-time data from a DirectQuery source while using imported historical data.
Option A is correct because a composite model lets some tables use DirectQuery (for real-time or frequently changing data) while other tables remain in Import mode, so you can blend live DirectQuery data with imported historical data in one model. Option B is correct because composite models support aggregation tables, allowing you to keep large fact tables in DirectQuery (or as detail tables) while importing pre-aggregated summary tables to accelerate queries. Option E is correct because the core purpose of a composite model is to combine DirectQuery sources with imported tables in the same semantic model, which is otherwise impossible in a pure Import or pure DirectQuery model. Option C is not a valid reason: composite storage mode does not inherently improve relationship performance, and relationship performance depends on cardinality, cross-filter direction, and model design rather than storage mode. Option D is not a valid reason: calculated tables are created with DAX and are always stored in Import mode, so they cannot be based directly on DirectQuery sources as a benefit of using a composite model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
To enable real-time data from a DirectQuery source while using imported historical data.
Why this is correct
This is a correct use case for composite models. In Power BI, a composite model allows you to combine tables from different storage modes in a single data model. A DirectQuery table can be configured to always pull the latest data from the source, while historical tables can be imported and refreshed on a schedule. This hybrid approach gives you real-time visibility into current transactions without giving up the performance benefits of pre-loaded historical data.
- ✓
To use aggregations on large fact tables while keeping other tables imported.
Why this is correct
A composite model is the enabling technology for this scenario. In Power BI, you can place aggregations on a large DirectQuery fact table, and the model will use those pre-aggregated summaries for high-level queries, while falling back to the underlying source for detailed queries. Other tables that are imported are stored completely in memory, so they are not affected by the aggregation design. This lets you balance performance and query latency across mixed storage modes.
- ✗
To improve relationship performance between tables.
Why it's wrong here
This is not a valid reason because composite models do not inherently improve relationship performance. In fact, relationships that span different storage modes (for example, between an imported table and a DirectQuery table) can introduce extra query overhead, because the engine may need to reconcile results across modes. If you need to speed up joins, techniques like reducing cardinality or creating proper star schemas are more effective, not simply switching to a composite model.
- ✗
To create calculated tables based on DirectQuery sources.
Why it's wrong here
This is not a valid reason because calculated tables are not supported in DirectQuery storage mode. In Power BI, calculated tables are evaluated only when the model is loaded, so they require the data to be available in memory via Import mode. If you attempt to base a calculated table on a DirectQuery source, you will get an error. Thus, composite models do not enable this; you would need to import the data first.
- ✓
To combine data from a DirectQuery source with imported tables.
Why this is correct
This is a key feature of composite models in Power BI. A composite model lets you add one or more DirectQuery sources to a model that already contains imported tables, and then build relationships between them. This allows you to combine, for example, high-volume source data that must be queried live with smaller reference or historical tables that are stored locally. It is the fundamental capability that makes mixed-mode data modeling possible.
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Written by Johnson Ajibi, MSc IT Security
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