Which statement correctly describes the difference between FILLET and CHAMFER when editing edges of 3D solids in AutoCAD?
Think about how a corner looks after applying each tool: one is curved, the other is angled.
FILLET creates a rounded edge by adding a smooth curve between two edges. CHAMFER cuts the edge at an angle, creating a flat, beveled surface.
You have a table 'Edges' with columns: EdgeID, SolidID, IsFilleted (TRUE/FALSE). Which DAX measure correctly counts how many edges are filleted for each solid?
Think about counting rows where a condition is true using a sum of 1s and 0s.
SUMX iterates over each row, adding 1 if IsFilleted is TRUE, else 0, effectively counting filleted edges. COUNTROWS with FILTER needs to be wrapped properly; option A is the simplest correct syntax.
You want to create a dashboard showing the count of filleted and chamfered edges per 3D solid. Which visualization type is best to clearly compare these counts side by side?
Think about comparing two categories side by side for each solid.
A stacked bar chart allows easy comparison of filleted and chamfered edge counts per solid in one view. Pie charts and line charts do not compare categories per solid effectively. Scatter plots show correlation, not counts per category.
Given this DAX measure to count chamfered edges:ChamferCount = COUNTROWS(FILTER(Edges, Edges[IsChamfered] = TRUE))
Why does this measure return an error?
ChamferCount = COUNTROWS(FILTER(Edges, Edges[IsChamfered] = TRUE()))
Check the syntax for boolean TRUE in DAX.
In DAX, TRUE is a function and must be written as TRUE() with parentheses. Omitting parentheses causes a syntax error.
You have a large dataset of 3D solids with edges and their modifications (fillet, chamfer). You want to optimize your data model for fast reporting on counts and types of edge modifications per solid. Which approach is best?
Think about star schema best practices for BI data models.
Creating a dimension table for EdgeModificationType and linking it to the Edges fact table allows efficient filtering, aggregation, and scalability. Storing JSON or unlinked tables complicates queries and slows performance. Flattening columns reduces flexibility and increases table width.
