PL-300 Prepare the data Practice Question
You have a Power BI semantic model that uses DirectQuery to an Azure Synapse Analytics dedicated SQL pool. The model is used by a real-time dashboard. Users report that the dashboard is slow. You need to improve query performance without changing the source system. Which action should you take?
⚠ Common exam trap
Many candidates assume performance improvements must come from data modeling changes (like aggregations or storage modes), but the question explicitly forbids changing the source system, so the only viable approach is to reduce the query load from the client side.
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
✓
Reduce the number of visuals on the dashboard and apply page-level filters
Reducing the number of visuals and applying page-level filters directly reduces the number of queries sent to the Azure Synapse Analytics dedicated SQL pool via DirectQuery. Since the source system cannot be changed, the only way to improve performance is to minimize the query load from the dashboard. Page-level filters ensure that only relevant data is queried, and fewer visuals mean fewer separate queries, which collectively reduces latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create aggregations on the fact table
Why it's wrong here
Creating aggregations on the fact table is not a quick fix for DirectQuery performance. In a DirectQuery model, a separate aggregation table (often using imported or dual storage mode) must be built and mapped via the Aggregations dialog before DAX can be redirected to it; simply adding summary rows or columns to the existing DirectQuery fact table does not change the query behavior and may actually require significant modeling effort. The performance benefit, if any, depends on the leaf-level granularity and the specific grouping, making this a strategic optimization rather than a simple reduction of live source queries.
- ✓
Reduce the number of visuals on the dashboard and apply page-level filters
Why this is correct
Reducing the number of visuals on a DirectQuery dashboard directly reduces the number of separate DAX queries that Power BI sends to the underlying data source, because each visual in DirectQuery mode issues its own live query when rendered. Applying page-level filters constrains the rowset returned for all visuals on that page, which lowers the data volume and speeds up each query. Together these actions are a simple, immediate way to lower query load without changing the data model.
- ✗
Enable dual storage mode for all tables
Why it's wrong here
Enabling dual storage mode for all tables in a DirectQuery model does not automatically improve performance and can actually complicate the model. Dual mode aggregates data into the Power BI in-memory cache for the imported partition, but for DirectQuery tables that still need live data, the source is still queried, so it does not eliminate the load; moreover, mixing storage modes can create ambiguity in relationships and security rules. It also increases memory pressure and refresh latency without guaranteeing fewer live queries.
- ✗
Disable the 'Reduce queries' option in Power BI Desktop
Why it's wrong here
Disabling the 'Reduce queries' option in Power BI Desktop would do the opposite of improving performance. These query-reduction settings are specifically designed to minimize the number of queries issued to data sources (for example, by suppressing automatic data relationships or limiting provisional queries), so disabling them would cause more queries to be generated. In a DirectQuery model, every extra source query worsens performance, making this option counterproductive.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.