PL-300 Visualize and analyze the data Practice Question
Which THREE actions can help optimize a Power BI report's performance? (Select THREE.)
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 a single page.
Option B is correct because each visual on a report page issues its own DAX queries against the dataset, so reducing the number of visuals per page lowers the number of concurrent queries and the rendering workload, improving report responsiveness. Option D is correct because pre-summarizing data in the source (for example, using SQL GROUP BY, views, or Power Query aggregation) reduces the volume of data loaded into the model and lets the engine scan smaller tables, which speeds up refresh and query execution. Option E is correct because cross-highlighting and cross-filtering force Power BI to re-query and re-render every affected visual whenever a selection is made; disabling them where interactivity isn't needed avoids that cascading query overhead. Option A is not appropriate because calculated columns are computed at refresh time, stored in the model, and consume memory, whereas measures are evaluated at query time and are the recommended approach for complex aggregations. Option C is not appropriate because importing all columns bloats the model, increases memory usage, and slows refresh and queries; best practice is to import only the columns actually needed.
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 calculated columns instead of measures for complex aggregations.
Why it's wrong here
Calculated columns are evaluated during data refresh and stored in memory, consuming RAM for every row, whereas measures are computed at query time only for the aggregations displayed. For complex aggregations, measures avoid materialising unnecessary intermediate values, reducing memory pressure and refresh overhead. This option is tempting because calculated columns are often used for row-level categorisation or static lookups, where pre-computation is genuinely beneficial.
- ✓
Reduce the number of visuals on a single page.
Why this is correct
Each visual on a Power BI page issues its own DAX query when the page loads. Rendering many visuals simultaneously consumes CPU, GPU, and memory, and also necessitates cross-visual interaction filters. Reducing the number of visuals per page lowers the number of parallel queries and the rendering workload, improving page load and interaction responsiveness. This is a classic report-level optimization enabled by better layout and navigation.
- ✗
Import all columns from source tables to avoid missing data.
Why it's wrong here
Importing every column from source tables inflates the VertiPaq data model with unnecessary columns, increasing storage footprint, refresh time, and memory usage. It does not guarantee data completeness; missing data is a data-quality issue that persists regardless of schema selection. Instead, you should import only columns that are used in reports, measures, or calculations, thus reducing dictionary compression overhead and speeding up refresh and query.
- ✓
Use aggregations in the data source (e.g., pre-summarize tables).
Why this is correct
Pre-aggregating tables at the data source, such as pre-summarizing daily sales to monthly totals, reduces the number of rows and the cardinality of key columns in the model. Fewer rows mean smaller dictionaries and faster scans in VertiPaq, and simpler DAX queries because aggregations are already computed. This complements, but is not a replacement for, proper star-schema design and can drastically cut query response times for high-level summary reports.
- ✓
Disable cross-highlighting and cross-filtering where not needed.
Why this is correct
Each visual's interaction behavior (cross-filtering and cross-highlighting) creates internal filter propagation that can trigger additional DAX evaluation across visuals. When a user clicks a data point, Power BI must propagate the selection to all other visuals, which is extra work. Disabling these interactions where not needed reduces the complexity of filter context and avoids unnecessary re-queries, especially on dashboards with many charts; this is a UI-level tuning that complements model-level optimization.
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