Explanation:

To build a standard scatter plot that plots individual dimensions across two continuous numerical performance axes, fields must be arranged to establish the coordinate plane.

Axes Mapping:
The vertical axis (Y-axis) displays negative and positive numerical values explicitly labeled Profit, placing it on the Rows shelf. The horizontal axis (X-axis) displays continuous numerical values labeled Sales, placing it on the Columns shelf.

De-aggregating the Marks:
When you first add two measures to the Rows and Columns shelves, Tableau automatically aggregates them into a single data mark point representing the grand total. To break this single point out into individual plot marks for every single person in the dataset, the Customer Name dimension must be dragged to the Detail property on the Marks card. This instructs Tableau to partition the view and draw a distinct dot for each unique customer.

Why Other Options Are Incorrect:

Placing Category on Detail:
While placing Category on Detail would break down the view, it would only yield a few data points (one mark per distinct product category) rather than the hundreds of highly distributed individual data points visible in the target visualization.

Swapping Rows and Columns:
Placing Sales on Rows and Profit on Columns would flip the orientation of the chart, forcing the Profit scale horizontally across the bottom and Sales vertically up the side, which directly contradicts the provided visual layout.

References:

Tableau Documentation (Create a Scatter Plot): "To create a scatter plot, place at least one measure on the Columns shelf and at least one measure on the Rows shelf... To disaggregate the data and display marks for individual dimension values, drag a dimension to the Detail shelf on the Marks card."

Explanation:

The first image shows a bar chart with individual bars for each Segment and Region combination. The second image shows the same chart but with shaded horizontal bands (areas of color) across the entire chart. These bands represent different ranges of values (e.g., below median, between median and upper quartile, etc.) and fill the background of the chart.

Why C is correct:
A reference band is a shaded area that spans the entire chart or a specific portion of it, indicating a range of values (e.g., between two constants, between percentiles, or between quartiles).
The second exhibit shows colored bands across the entire chart area, which is exactly what a reference band does.
Reference bands are commonly used to highlight ranges like quartiles, averages ± standard deviation, or custom value ranges.

Why other options are incorrect:

A. An average line:
An average line is a single horizontal or vertical line at the average value. The exhibit shows shaded regions, not just a single line. An average line would not create the multiple colored bands shown.

B. A reference line:
A reference line is a single line (e.g., at a specific value like a target or threshold). The exhibit shows shaded areas spanning ranges, not a single line.

D. A distribution band:
A distribution band is a specific type of reference band that shows the spread of data (e.g., standard deviation, confidence interval). While distribution bands are a type of reference band, the options are distinct—"Reference band" is the broader category, and the exhibit shows quartile bands, which are typically created using the Reference Band option with quartiles as the range.

Reference:

Tableau Help: Add Reference Lines, Bands, Distributions, and Box Plots – Reference bands are shaded areas that can be used to highlight a range of values, such as quartiles, percentiles, or custom ranges.

Explanation:

To create and configure a time series predictive model natively in Tableau Desktop, you must build the structural visualization framework before layering analytical modeling steps on top of it.

Action 1 (Build the Base View): Tableau's forecasting engine requires at least one date dimension and one continuous measure in the view to evaluate a time-series line or matrix. Placing Business Segment (to separate by segment), the continuous Month of Order Date (to meet the monthly historical trend requirement), and Sales onto the canvas creates the baseline visualization.

Action 2 (Generate the Model): Once the core visualization is built, switching to the Analytics pane and dragging the Forecast object directly onto the visual canvas instructs Tableau to run its exponential smoothing models (ETS) across the historical monthly milestones.

Action 3 (Refine Settings): After the initial forecast is generated, accessing the Forecast Options menu (via the indicator or right-clicking the view > Forecast > Forecast Options...) allows you to customize specific parameters, such as the exact forward-looking timeline length and the precision boundaries of the prediction intervals.

Why Other Options Are Incorrect:

"Create a calculated field that uses a table calculation..." and "Create a parameter to control the forecast length...": While parameters can be used to slice historical views or build custom math formulas, Tableau's built-in forecasting features do not require manual table calculation script syntax or parameters to compute baseline ETS models.

Options involving Trend Lines: A trend line evaluates static linear, logarithmic, or polynomial geometric lines of best fit across existing historical coordinates to show overall direction. It does not generate forward-looking, segment-isolated time-series forecasts.

References:

Tableau Documentation (Forecasting): "Forecasting requires a view that uses at least one date dimension and one measure... To add a forecast, click the Analytics tab, drag Forecast into the view, and then drop it on the Forecast area."

Explanation:

The table in the exhibit shows quarterly totals for each company, with a subtotal row at the end of each company's data (indicated by the bold or underlined "Total" rows). These are subtotals because they represent totals for each company (a subgroup) rather than a grand total for the entire table.

Why A is correct:
Add All Subtotals enables subtotals for every dimension in the view. In this case, the view has a dimension (Company), and enabling "All Subtotals" adds a subtotal row for each company, showing the total across all quarters for that company.
The question asks for "quarterly totals" for each company, which is exactly what subtotals do—they aggregate measures within each group.

Why other options are incorrect:

B. Show Column Grand Totals:
This adds a total column (e.g., a "Total" column at the right side of the table) that sums across all quarters for all companies combined. It does not add individual rows per company.

C. Show Row Grand Totals:
This adds a grand total row at the bottom of the table, summing all companies and quarters together. It does not create per-company subtotal rows.

D. Add Pane Totals:
This option is not a standard Tableau setting. Tableau uses "Subtotals" and "Grand Totals" as the primary aggregation options. "Pane Totals" is not a recognized term in this context.

Reference:

Tableau Help: Add Totals, Subtotals, and Grand Totals – Subtotals are used to aggregate data at the level of individual dimension members (e.g., each Company), while Grand Totals aggregate the entire view. "Add All Subtotals" adds subtotals for all dimensions in the view.

Explanation:

To calculate the percentage of total sales within each specific sub-grouping or visual compartment—in this case, for each category within every region on an annual basis—you must set the table calculation's scope to restart at the boundary of each partition.

The Visual Layout (The Panes): In Tableau crosstabs, columns typically house the time series (YEAR(Order Date)) and outer segmentation headers (Region), while rows house the item classification (Category). This intersection creates distinct bounding boxes called Panes (representing a specific Region for a specific Year).

The Table Calculation Direction: Choosing Pane (down) instructs Tableau to compute the table calculation vertically within the boundaries of each individual pane. The calculation calculates the percentage for each category and then resets to zero at the start of the next pane (the next year or region combination), ensuring that the categories within that specific region and year sum to exactly 100%.

Why Other Options Are Incorrect:

Table (down): This computes the percentage relative to the grand total of the entire column across all years and regions combined, preventing individual panes from summing to 100%.

Pane (across):This moves horizontally across the columns (along the time series or regions) within a single category tier, rather than down through the categories themselves.

Cell: This treats every single individual intersection cell as its own independent partition, resulting in every mark evaluating to exactly 100%.

References:

Tableau Documentation (Table Calculations: Scope and Direction): "Choosing Pane (down) computes down the height of the pane and resets for every pane. The values within each pane sum to 100%."