When you used a prebuilt subject area to create a custom workbook in FDI, what recommendation optimizes performance?

When you used a prebuilt subject area to create a custom workbook in FDI, what recommendation optimizes performance?

• A. Use the recommended filters for the prebuilt workbook and keep the OAC brushing feature turned off.
• B. Disable all filters for faster rendering.
• C. Enable unlimited brushing for interactivity.
• D. Use random filters to vary results.

Answer: (A)

When you build a custom workbook from a prebuilt subject area, performance is best when you lean on the filters that the design of that subject area expects and avoid extra interactivity that adds rendering work. The recommended filters are chosen to restrict data to what’s most relevant, which reduces query complexity and speeds up visual rendering. Keeping the brushing feature off removes the additional processing needed to highlight and propagate selections across multiple visuals, especially with large datasets. Together, these choices minimize the workload on the analytics engine while preserving the core insights from the prebuilt subject area. Using fewer or no filters can cause more data to be processed, slowing things down, and turning brushing on adds interactive overhead that can further degrade performance. Random filters introduce unpredictability and may disrupt the intended analysis, offering little benefit to performance.

When you build a custom workbook from a prebuilt subject area, performance is best when you lean on the filters that the design of that subject area expects and avoid extra interactivity that adds rendering work. The recommended filters are chosen to restrict data to what’s most relevant, which reduces query complexity and speeds up visual rendering. Keeping the brushing feature off removes the additional processing needed to highlight and propagate selections across multiple visuals, especially with large datasets. Together, these choices minimize the workload on the analytics engine while preserving the core insights from the prebuilt subject area.

Using fewer or no filters can cause more data to be processed, slowing things down, and turning brushing on adds interactive overhead that can further degrade performance. Random filters introduce unpredictability and may disrupt the intended analysis, offering little benefit to performance.