PL-300 Model the data Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Import each source into separate Power BI Desktop files and manually update.

  Why it's wrong here

  Separate Power BI Desktop files create siloed reports with no shared data model, forcing analysts to manually cross-reference results and reconcile discrepancies. Each file runs its own refresh, so there is no centralized schedule, and any change to a source requires manually re-importing or copying data. This approach cannot scale beyond a handful of files without introducing versioning chaos and broken relationships.

• ✗

  Use Excel Online as the single source and import all data into it first.

  Why it's wrong here

  Excel Online lacks the robust data integration features needed to merge multiple sources: it enforces a 1,048,576-row limit per worksheet and does not include native connectors for common databases like SQL Server. Loading diverse data into Excel first adds an unnecessary ETL hop, amplifies data-size restrictions, and still requires a separate refresh step in Power BI. Excel is a presentation tool, not an integration hub, so it creates a fragile bottleneck.

• ✓

  Use Power Query to combine data from all sources into a single dataset, then schedule a daily refresh in the Power BI service.

  Why this is correct

  Power Query (Get Data) provides native connectors and a rich transformation environment to merge, append, and shape datasets from disparate sources into a single, consistent model. Publishing that model once and configuring a daily scheduled refresh in the Power BI service centralizes maintenance and ensures all visuals receive updated data automatically. This approach aligns with best practices for scalable self-service BI because the refresh burden is handled by the service, not a user.

• ✗

  Create separate datasets for each source and use composite models with DirectQuery.

  Why it's wrong here

  Creating distinct datasets per source forces each dataset to have its own refresh schedule, increasing management overhead and risk of stale data. Composite models that combine DirectQuery and Import introduce performance penalties and require all involved sources to support the same authentication and query-folding capabilities, which many on-premises sources do not. This design adds architectural complexity without the benefit of a single cohesive dataset.

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