PL-300 Prepare the data Practice Question
You are a data analyst for a global retail company. The company uses Power BI Premium capacity. You are building a dataset that combines sales data from three sources: 1. An Azure SQL Database that stores transactional sales data (10 million rows per day, retained for 5 years). 2. A SharePoint Online folder containing monthly Excel reports from regional offices (each report has a different structure). 3. A Dataverse table that contains customer feedback scores.
Requirements: - The dataset must support near real-time reporting for the current month's sales (maximum 15-minute latency). - Historical sales data (older than current month) can be refreshed daily. - Customer feedback scores should be updated every hour. - The Excel reports from SharePoint must be combined into a single table with consistent columns. - The final dataset should be optimized for fast query performance.
You need to design the data preparation strategy. What should you do?
⚠ Common exam trap
Test-takers frequently choose Import mode for everything (Option B) without realizing the 48-refresh-per-day limit on Power BI Premium, which prevents 15-minute refreshes, or they may overlook composite models as the only way to combine real-time and historical data efficiently.
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 a composite model: DirectQuery for the current month's sales data from Azure SQL, and Import mode for historical sales (with incremental refresh) and for customer feedback (with hourly refresh). Combine SharePoint files using Power Query and load them into the model using Import mode. Set up a DirectQuery connection for near real-time.
It uses a composite model to meet all requirements: DirectQuery for near real-time current-month sales (≤15-minute latency), Import mode with incremental refresh for historical sales (daily refresh), Import mode for customer feedback (hourly refresh), and Power Query to combine SharePoint Excel files into a consistent table. This approach balances real-time needs with query performance and refresh flexibility, leveraging Power BI Premium's composite model capabilities.
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 DirectQuery for all data sources and create views in Azure SQL to transform the SharePoint and Dataverse data. Use Power Query to combine SharePoint files in a view.
Why it's wrong here
DirectQuery is not universally supported: Azure SQL supports it, but SharePoint and Dataverse do not. SharePoint lacks DirectQuery entirely, so using Power Query to combine files into a view in Azure SQL would necessitate moving data to a separate source, defeating the purpose of DirectQuery's zero-copy querying. Even if Dataverse could be queried directly, this approach fails to address the requirement for a composite model that balances near real-time with historical data, and it introduces needless complexity without a functional path for SharePoint.
- ✗
Import all data into Power BI using Import mode. Schedule refreshes every 15 minutes for the current month and daily for historical data.
Why it's wrong here
Import mode stores data in the Power BI in-memory engine, so it cannot provide true near real-time access to the current month's sales. Scheduled refreshes are constrained by capacity and refresh duration; 15-minute intervals are not supported for Import mode in most Shared or even Premium capacities, and each refresh would re-query the entire Azure SQL dataset, making it slow and resource-intensive. This approach also lacks the incremental refresh feature needed to efficiently manage historical data, and it fails to meet the business's explicit near real-time requirement for current-month data.
- ✓
Use a composite model: DirectQuery for the current month's sales data from Azure SQL, and Import mode for historical sales (with incremental refresh) and for customer feedback (with hourly refresh). Combine SharePoint files using Power Query and load them into the model using Import mode. Set up a DirectQuery connection for near real-time.
Why this is correct
This is correct because it uses a composite model to combine DirectQuery and Import modes: DirectQuery on Azure SQL for the current month's sales queries the source directly, providing near real-time visibility without data duplication or refresh latency. Historical sales are imported with incremental refresh, which partitions the data and only loads new or changed partitions, dramatically reducing refresh time and resource use. Customer feedback is imported on an hourly schedule since it does not require sub-hour latency, and SharePoint files are combined via Power Query and imported, as they lack DirectQuery support. This design maximizes performance while meeting all stated latency and freshness requirements, and it is a recognized best practice for hybrid data scenarios in Power BI.
- ✗
Use Azure Data Factory to copy all data to Azure SQL Database, then connect Power BI using DirectQuery.
Why it's wrong here
Azure Data Factory (ADF) can copy data from all sources into Azure SQL, and DirectQuery would then provide a consistent query layer, but this introduces batch latency: ADF pipelines run on a schedule, not continuously, so the SQL database would be stale between runs, failing the near real-time requirement. Additionally, copying all data defeats the purpose of DirectQuery by creating a full duplicate in Azure SQL, adding storage cost and copy time. While ADF could integrate SharePoint files, the extra ETL layer and schedule-based updates make this solution slower and more complex than a composite model, and it does not leverage Power BI's native incremental refresh or mixed storage capabilities.
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