When creating a drawing from an assembly in SolidWorks, which view type best shows the internal components without hiding any parts?
Think about which view cuts through the assembly to reveal inside parts.
A Section View slices through the assembly, showing internal components clearly. Exploded views separate parts but don't show internal details. Detail and Projected views focus on specific areas or projections.
You have a SolidWorks assembly drawing with multiple parts. Using a BI tool, you want to calculate the total count of unique parts shown in the drawing. Which DAX measure correctly counts unique part IDs from the 'Parts' table?
Total Unique Parts = DISTINCTCOUNT(Parts[PartID])
Count only unique part IDs, not all rows.
DISTINCTCOUNT counts unique values in a column. COUNT counts all non-blank values, SUM adds numeric values, and COUNTROWS counts all rows regardless of uniqueness.
You want to create a dashboard visualization showing the number of parts per sub-assembly in a SolidWorks assembly drawing. Which visualization type best communicates this distribution clearly to non-technical users?
Think about comparing quantities across categories with clear labels.
A Stacked Bar Chart shows part counts per sub-assembly clearly and allows easy comparison. Pie Charts can be hard to compare precisely, Scatter Plots show relationships, and Line Charts show trends over time.
Your BI report shows fewer parts than expected from a SolidWorks assembly drawing. The DAX measure used is:
Parts Count = COUNTROWS(FILTER(Parts, Parts[IsVisible] = TRUE()))
Why might this measure undercount parts?
Consider data accuracy and column values.
If the IsVisible column is not updated when parts are hidden in the drawing, the filter excludes parts incorrectly, causing undercount. COUNTROWS with FILTER is valid. SUM is not appropriate here. Parts is a table, so FILTER works.
You have a large SolidWorks assembly drawing with thousands of parts and sub-assemblies. Your BI dashboard is slow when filtering by sub-assembly. Which data modeling approach best improves performance?
Think about how BI tools handle relationships and filtering efficiently.
A star schema organizes data into a central fact table and related dimension tables, enabling fast filtering and aggregation. Flat tables can be large and slow. Disconnected tables require complex calculations and reduce performance. No relationships limit filtering capabilities.
