Which statement best explains how lineweight settings affect the readability of a technical drawing?
Think about how different line thicknesses help you see important parts clearly.
Lineweight settings vary line thickness to highlight or de-emphasize parts of a drawing, making it easier to understand.
Given a dataset of drawing layers with lineweight values in millimeters, which DAX measure correctly calculates the average lineweight across all layers?
Layers = DATATABLE("Layer", STRING, "Lineweight_mm", DOUBLE, {{"Walls", 0.5}, {"Doors", 0.35}, {"Windows", 0.25}, {"Furniture", 0.15}})
Consider how to calculate average ignoring any filters.
Option A uses CALCULATE with ALL to ensure average is over all layers regardless of filters.
You want to create a dashboard showing the distribution of lineweights used in different drawing layers. Which visualization type is best to clearly show frequency of each lineweight?
Think about which chart type best shows counts per category.
A bar chart clearly shows frequency counts for each lineweight category, making it easy to compare.
You manage lineweight settings for multiple projects, each with different layers and lineweight values. Which data model design best supports easy reporting and comparison?
Consider normalization and flexibility for reporting.
Option A normalizes data into Projects, Layers, and Lineweights tables, enabling flexible and scalable reporting.
A DAX measure intended to calculate total lineweight per project is returning incorrect results. Identify the error in this measure:
Total Lineweight = SUMX(FILTER(Layers, Layers[ProjectID] = SELECTEDVALUE(Projects[ProjectID])), Layers[Lineweight_mm])
Think about filter context and how FILTER works inside SUMX.
FILTER without ALL keeps existing filters, which may exclude needed rows. Adding ALL removes filters for correct calculation.
