Which factor most directly affects the required trace width in a PCB to safely carry a given current?
Think about what physically carries the current in the PCB.
The thickness of the copper layer affects the cross-sectional area of the trace, which determines how much current it can safely carry without overheating.
You need to design a PCB trace to carry 3A of current on an external layer with 1 oz copper thickness. Which trace width is closest to the recommended value according to standard calculators?
Standard trace width calculators suggest wider traces for higher current on external layers.
For 3A on 1 oz copper external layer, typical calculators recommend around 30 mils trace width to keep temperature rise within safe limits.
Given a table 'Traces' with columns 'Layer' and 'Width_mils', which DAX measure correctly calculates the average trace width per layer?
Average Trace Width = AVERAGEX(VALUES(Traces[Layer]), CALCULATE(AVERAGE(Traces[Width_mils])))
Think about how to calculate average per group in DAX.
Option A uses AVERAGEX over distinct layers and calculates average width per layer correctly. Option A changes filter context incorrectly. Options A and C do not group by layer.
You want to show how trace widths vary across different PCB layers. Which visualization type best communicates this distribution clearly?
Consider which chart type shows spread and variation well.
Box plots effectively show distribution, median, and outliers of trace widths per layer. Stacked bars and pie charts do not show distribution details. Line charts are not suitable here.
A BI report shows average trace width per layer, but the values are unexpectedly low. The DAX measure used is:AvgWidth = AVERAGE(Traces[Width_mils])
What is the most likely cause?
Think about how aggregation works without grouping.
Using AVERAGE without grouping ignores layers, so the report shows overall average, not per layer. This causes unexpectedly low values when viewed by layer.
