PL-300 Visualize and analyze the data Practice Question
Which THREE actions can you take to improve the performance of a slow Power BI report that uses multiple visuals on a single page?
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 the Performance Analyzer to identify and optimize the slowest visuals.
Option B is correct because the Performance Analyzer in Power BI Desktop records the DAX query, visual display, and other timing metrics for each visual, letting you pinpoint exactly which visuals are slowest and target optimization efforts. Option D is correct because each field added to a visual increases the size of the DAX query result and the rendering work, so trimming visuals to only the necessary fields reduces query and render time. Option E is correct because cross-filtering and cross-highlighting force dependent visuals to re-query and re-render whenever a selection is made, so disabling interactions between unrelated visuals cuts unnecessary query and rendering overhead. Option A is not correct because refresh frequency affects data latency and load on the source, not the rendering performance of a report page. Option C is not correct because adding more calculated measures generally increases model complexity and query cost rather than precomputing aggregations; pre-aggregation is achieved through import mode, aggregation tables, or summary tables, not by adding calculated measures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the frequency of data refreshes to reduce data latency.
Why it's wrong here
Increasing the frequency of data refreshes addresses data freshness, not report rendering performance. Every refresh consumes CPU, memory, and concurrency slots in the dataset, which can actually degrade query performance during and after the refresh window. Since the bottleneck is visual rendering or DAX query execution, lowering data latency has no effect on the response time of an already loaded report.
- ✓
Use the Performance Analyzer to identify and optimize the slowest visuals.
Why this is correct
The Performance Analyzer in Power BI Desktop records the time spent on each stage of a visual's update, including DAX query execution, Visual Show time, and Layout time. By measuring specific visuals rather than guessing, you can identify the most expensive ones and apply targeted optimizations like rewriting DAX, simplifying the visual, or removing unnecessary record-level details. This evidence-based approach is essential because performance issues often stem from a few outliers, not the whole report.
- ✗
Add more calculated measures to precompute aggregations.
Why it's wrong here
Calculated measures are formulas that are evaluated on the fly in the current filter context, so they do not precompute or store aggregations. Adding more measures increases the amount of computation required during rendering, because each measure can trigger additional storage-engine scans and CPU cycles in the formula engine. Precomputed storage would require a calculated table or an aggregations table, not a measure, making this a misunderstanding of how measures work.
- ✓
Reduce the number of fields used in each visual to only those necessary.
Why this is correct
Every field included in a visual expands the data that must be retrieved from the dataset and processed for display, especially high-cardinality columns. Reducing the number of fields to only those required lowers the volume of data transferred to the client and shortens the time spent on sorting, grouping, and layout. Focus on removing fields whose cardinality is high but whose visual contribution is low, as this often yields the greatest performance gain.
- ✓
Reduce the number of visual interactions by disabling cross-filtering between unrelated visuals.
Why this is correct
By default, selecting a data point in one visual cross-filtera all other visuals on the page, and each affected visual may trigger its own DAX query, resulting in a cascade of queries. Disabling cross-filtering between unrelated visuals eliminates those extra queries, reducing the total load per interaction and making the report feel more responsive. This optimization is effective because it directly reduces the number of queries spawned during analysis, rather than making each query itself faster.
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