Explore and Analyze Data

An analyst builds a parameter called Sales Threshold so viewers can type a value and immediately see which regions clear it. After creating the parameter, viewers report they cannot change the value from the published dashboard. What must the analyst do so the value becomes adjustable in the view?

• ADrag the parameter onto the Filters shelf so that the dashboard exposes it as an editable quick filter for viewers.
• BConvert the parameter into a continuous measure and add it to the Detail property of the Marks card to register it.
• CShow the parameter control by right-clicking the parameter in the Data pane and selecting Show Parameter, then placing the resulting card on the view.check_circle Correct
• DRepublish the workbook with the parameter pinned, because parameters become interactive automatically once the workbook is on the server.

Why A is wrong: Parameters cannot be placed on the Filters shelf because they are not data fields, so this action is not possible and would not surface an editable input.

Why B is wrong: A parameter is a standalone value, not a measure that can be aggregated on the Marks card, and adding fields to Detail does nothing to make a parameter editable.

Why C is correct: Creating a parameter only stores a single editable value; it is not interactive until its control is displayed, so showing the parameter control is exactly what exposes the input to viewers.

Why D is wrong: Publishing alone never reveals a parameter; if its control was never shown, viewers see no input regardless of where the workbook lives.

A dashboard has a context filter on Order Date for 2024 and a normal dimension filter keeping only the Corporate segment. A measure uses { FIXED [Region] : SUM([Sales]) }. Considering Tableau's order of operations, which records does this FIXED value reflect?

{ FIXED [Region] : SUM([Sales]) }

• AAll records in the source, because FIXED runs before every filter regardless of type.
• BOnly 2024 records across all segments, because the context filter applies before FIXED but the segment dimension filter applies after.check_circle Correct
• COnly 2024 Corporate records, because both filters are applied before any LOD expression evaluates.
• DOnly Corporate records across all years, because dimension filters bind to FIXED but date context filters do not.

Why A is wrong: FIXED runs before dimension filters but after context filters, so the 2024 context filter does narrow the records it sees.

Why B is correct: Context filters precede FIXED in the order of operations so 2024 is honoured, while the ordinary Corporate dimension filter is applied afterwards and so does not narrow the FIXED result.

Why C is wrong: The Corporate filter is an ordinary dimension filter that applies after FIXED, so it does not restrict the FIXED computation.

Why D is wrong: This reverses the precedence: context filters apply before FIXED while ordinary dimension filters apply after, so the year is constrained and the segment is not.

A view has Region and Segment on rows with SUM(Sales) shown. An analyst adds { EXCLUDE [Segment] : SUM([Sales]) } to compute each Segment's share of its Region total. What value does this EXCLUDE expression return on every row?

{ EXCLUDE [Segment] : SUM([Sales]) }

• AThe Segment subtotal of Sales summed across all Regions, repeated on each row.
• BThe grand total of Sales across every Region and Segment, because EXCLUDE strips both dimensions.
• CThe individual Segment value within its Region, unchanged from the native SUM(Sales) on the row.
• DThe Region subtotal of Sales, repeated identically across all Segment rows within that Region.check_circle Correct

Why A is wrong: That result would come from excluding Region, not Segment, so it inverts which dimension is removed from the grain.

Why B is wrong: EXCLUDE removes only the dimension it names, here Segment, so Region remains in the grain and the result is not the overall grand total.

Why C is wrong: Leaving the value unchanged would mean nothing was excluded, but EXCLUDE [Segment] deliberately coarsens the grain to the Region level.

Why D is correct: EXCLUDE drops Segment from the grain, leaving Region as the effective dimension, so every Segment row in a Region shows that Region's full subtotal.

A view has Category on Rows and Sub-Category nested beneath it, with Percent of Total applied to SUM(Sales). The analyst sets Compute Using to Pane (down). Within the Furniture category, what denominator does each sub-category percentage use?

Rows:  Category > Sub-Category
Marks: Percent of Total of SUM(Sales), Compute Using = Pane (down)

• AThe total sales of the Furniture category only, so the Furniture sub-categories sum to one hundred percent among themselves.check_circle Correct
• BThe grand total of sales across every category, so all sub-category percentages in the whole view sum to one hundred.
• CThe sales of the single largest sub-category within Furniture, so the leading sub-category shows one hundred percent.
• DThe total sales of the matching sub-category summed across all categories, comparing it horizontally between categories.

Why A is correct: Pane (down) confines the percent-of-total denominator to the current pane, which here is one category block, so each sub-category is divided by its own category total.

Why B is wrong: A grand-total denominator corresponds to Table rather than Pane; Pane restricts the scope to the innermost partition, so this overstates the denominator.

Why C is wrong: Percent of total never uses the maximum value as the denominator; that would describe an index against the top performer, which is a different calculation entirely.

Why D is wrong: Comparing the same sub-category across categories implies an across direction, but Pane (down) partitions by category and addresses sub-categories downward, so no cross-category comparison occurs.

A team needs a reusable field that keeps changing which members it contains as sales figures move, and they want to use it to filter several worksheets to a dynamic top-performing cohort. Which structuring feature best fits a membership that must update automatically with the data?

• AA condition-based or top-N set, because such a set recomputes its members automatically as the data changes.check_circle Correct
• BA group, because groups recalculate their members whenever a measure crosses a chosen threshold.
• CA bin, because bins reassign members to different buckets as the underlying measure changes.
• DA hierarchy, because expanding a hierarchy automatically refreshes which members qualify for the cohort.

Why A is correct: A set defined by a condition or a top-N rule is dynamic and reusable across sheets, recalculating membership as sales move, which matches the requirement for an automatically updating cohort.

Why B is wrong: Groups are static collections of manually chosen members; they do not respond to changing measures, so this attributes dynamic behaviour to the wrong feature.

Why C is wrong: Although bin assignment depends on a value, a bin classifies every value into ranges rather than producing a single dynamic cohort, so it does not serve as a dynamic membership filter the way the requirement describes.

Why D is wrong: A hierarchy only arranges drill levels; it has no membership rule and cannot define a top-performing cohort, so it is the wrong tool for this need.