Which statement best describes the purpose of a projected view in a technical drawing?
Think about how projected views relate to existing views in a drawing.
Projected views are created by projecting features from an existing view to show additional sides or details, maintaining alignment and scale.
Given a dataset of technical drawings with a column 'ViewType' containing values 'Main', 'Projected', and 'Auxiliary', which DAX measure correctly counts only the projected views?
Views = COUNTROWS(FILTER(Drawings, Drawings[ViewType] = "Projected"))Use FILTER to select only rows where ViewType is 'Projected'.
Option A uses FILTER correctly to count rows where ViewType equals 'Projected'. Option A has incorrect filter syntax, C counts non-projected views, and D is valid but less efficient.
You have data showing counts of auxiliary views per drawing category. Which visualization best helps compare these counts clearly?
Consider clarity and ease of comparing counts across categories.
A simple bar chart allows direct comparison of auxiliary view counts per category. Pie charts are less effective for comparing multiple categories, stacked bars mix data types, and line charts are for trends over time.
Given this DAX measure intended to count auxiliary views:AuxViews = COUNTROWS(FILTER(Drawings, Drawings[ViewType] == "Auxiliary"))
What is the error?
Check the correct syntax for equality in DAX.
DAX uses a single equals sign '=' for comparison, not '=='. Using '==' causes a syntax error.
You are tasked with creating a dashboard to analyze projected and auxiliary views across multiple projects. Which combination of visualizations and filters will best support detailed and summary insights?
Think about how users explore data from summary to detail.
Option A provides interactive filtering by project, clear comparison of view types, and detailed data for deeper analysis. Other options lack interactivity or detail needed for expert analysis.
