You have a table named Parts with columns PartID, MaterialCost, and LaborCost. You want to create a DAX measure that calculates the total estimated cost by summing material and labor costs for all parts.
Which DAX expression correctly calculates this total estimated cost?
Think about how to sum two columns row by row before adding all rows.
Option C uses SUMX to add material and labor costs for each row, then sums all rows. Option C sums columns separately then adds totals, which works but can be less efficient. Option C is invalid because SUM cannot sum an expression. Option C has syntax error because Parts[LaborCost] is outside SUMX.
You want to show the cost breakdown by material type in a dashboard. Which visualization type is best to clearly show the proportion of total cost each material contributes?
Think about which chart best shows parts of a whole.
A pie chart (Option D) clearly shows how each material's cost contributes to the total cost as parts of a whole. Stacked bar charts and line charts are better for trends over time. Scatter plots show relationships between two variables, not proportions.
You need to design a data model for a costing tool that estimates manufacturing costs. The data includes parts, materials, labor rates, and machine times. Which modeling approach best supports flexible cost calculations and easy updates?
Think about normalization and relationships for maintainability.
Option B is best because separate related tables linked by keys allow flexible updates and accurate calculations. Option B is hard to maintain and update. Option B mixes unrelated data reducing clarity. Option B lacks relationships, making calculations difficult.
In your costing tool, some parts have missing labor cost data. You want to estimate total cost without errors and avoid underestimating. Which approach is best?
Think about how to avoid bias and keep estimates realistic.
Option A is best because imputing missing labor costs with averages from similar parts avoids underestimating and keeps data consistent. Option A excludes data reducing completeness. Option A treats missing as zero, underestimating cost. Option A ignores missing data, causing inaccurate totals.
You wrote this DAX measure to calculate average cost per part:
Average Cost = AVERAGE(Parts[MaterialCost] + Parts[LaborCost])
When you use it, you get an error. What is the cause?
Average Cost = AVERAGE(Parts[MaterialCost] + Parts[LaborCost])
Check what arguments AVERAGE accepts.
AVERAGE expects a single column reference, not an expression adding two columns. You must use SUMX to add columns row by row before averaging. Option A is close but the error is specifically about AVERAGE's argument. Option A is incorrect because syntax causes error. Option A is false; AVERAGE works on numeric columns.
