In SolidWorks, a design table controls configurations by linking parameters to Excel cells. What is the main advantage of using a design table for parameter-driven configurations?
Think about how Excel integration helps manage many configurations easily.
Design tables let you control many configurations by editing an Excel sheet, making bulk changes simple and efficient.
Given a table 'Configurations' with a column 'ConfigName', which DAX measure correctly counts unique configurations driven by parameters?
Measure = DISTINCTCOUNT(Configurations[ConfigName])
Count unique names, not total rows.
DISTINCTCOUNT counts unique configuration names, which is what we want to know how many different configurations exist.
You have multiple configurations with varying parameter values. Which visualization best shows how a parameter affects a dimension across configurations?
Think about showing trends or changes over configurations.
A line chart clearly shows how a parameter changes across configurations, making trends easy to spot.
In a BI model, you want to link design table parameters to configurations. Which data modeling approach is best?
Think about normalization and relationships for flexible analysis.
A fact table with configuration IDs and parameter values linked to a configuration dimension allows scalable and flexible analysis.
You wrote this DAX measure to count configurations with a specific parameter value:CountParam = COUNTROWS(FILTER(Configurations, Configurations[Param] = "High"))
But the count is higher than expected. What is the likely cause?
Are you counting rows or unique configurations?
COUNTROWS counts all filtered rows. If there are duplicates or multiple rows per configuration, the count will be higher than the number of unique configurations. Use DISTINCTCOUNT instead.
