Explanation:

✅ When you sign in to a Tableau Server site from Tableau Desktop and search for a published data source, the list of available data sources is filtered based on your permissions. Specifically, Tableau Server restricts visibility to only those data sources that are published to the site and located within projects (or folders) where you have been granted access. This is a key aspect of Tableau’s permission model, which ensures users only see content they are authorized to view or interact with. Here’s why the other options are incorrect:

A. All the data sources published to the site: This is incorrect because it does not account for permissions. Even if a data source is published to the site, you won’t see it unless you have access to the project or folder containing it.

B. All the data sources published to the Tableau Server: This is incorrect because it refers to the entire Tableau Server, which may host multiple sites. You are signed into a specific site, so only data sources on that site (and within accessible folders) are relevant.

D. All the data sources published to the Tableau Server within the folders to which you have access: This is incorrect because it references the entire Tableau Server rather than the specific site you are signed into. Tableau Server can have multiple sites, and data sources are scoped to a specific site.

ℹ️ Reference:

➡️ Tableau Help Documentation: The Tableau Server Help page on “Permissions” explains that access to content, including data sources, is controlled by permissions set at the project or folder level within a site. Only data sources in projects where a user has view or connect permissions will appear when searching from Tableau Desktop (Tableau Server Help: Permissions).

➡️ Salesforce Tableau Data Analyst Exam Guide: The section on “Publish and Manage Content” (10% of the exam) covers managing and accessing published data sources, emphasizing the role of permissions in determining visibility (Tableau Certified Data Analyst Exam Guide, October 2024).

➡️ Trailhead Module: The Tableau Data Analyst Certification Prep Guide (Unit 4: Publish and Manage Content) includes scenarios on accessing published data sources, reinforcing that visibility is limited to projects with granted permissions.

Explanation:

In Tableau Prep, the "Left only" join returns all the rows from the primary (left) table (Table1 in your case) that do not have matching values in the secondary (right) table (Table2). The result includes these exclusive values from Table1, and any columns from Table2 in the joined output will appear as nulls for these rows—since there is no matching data in Table2 to populate them.

This differs from a standard "Left" join (Option C), which includes all rows from the left table but also brings in any that have matches in the right table. The "Left only" join isolates only those rows from Table1 that have no match in Table2, which is exactly what your scenario asks for.

➡️ "Inner" join (Option A) keeps only matching rows.
➡️ "Left" join (Option C) keeps all from the primary and fills non-matches with nulls but also includes matches.
➡️ "Full outer" (Option D) brings all rows from both tables, matched and unmatched.
➡️ "Union" (Option E) stacks rows and is not a join in the context of matching fields.

Reference:
➡️ Tableau Prep's own documentation and tutorials clarify that "Left only" is the join type to get just the non-matching records from the primary table with nulls in the secondary fields.

Explanation:

✅ Correct Answer: C. Right-click on the rightmost dimension, choose Sort, then select nested in the options.
This is the correct approach when you want to sort values like Sales across multiple dimensions in Tableau (e.g., Region → Category → Sub-Category). In Tableau, sorting is hierarchical when multiple dimensions are placed on Rows or Columns. To preserve this hierarchy while sorting based on a measure like Sales, you must sort within each level rather than globally. This is done through a nested sort, which means Tableau will sort each instance of the lower-level dimension (the rightmost one on the shelf) within the context of the higher-level dimensions. You apply this by right-clicking on the last dimension in the hierarchy (e.g., Sub-Category), choosing Sort, then selecting Field, choosing Sales as the sorting field, and enabling Nested sort. This maintains the proper grouping structure while ordering values correctly within each group.

❌ Option A: Use the Sets feature to combine dimensions and then sort one of the fields.
Sets in Tableau are used primarily for comparing subsets of data or creating dynamic groups based on conditions or selections. While they are powerful tools for filtering and segmenting data, they are not intended for sorting purposes. Sets can define which data is in or out of focus, but not how that data is ordered across a hierarchical structure. Trying to combine multiple dimensions into a set and then sort one of the fields would not produce the desired nested or contextual sort needed across multiple dimensions. Furthermore, sets are binary in nature (i.e., a value is either in the set or not), which makes them unsuitable for ranking or ordering operations across a complex hierarchy.

❌ Option B: Use the Group feature to combine dimensions and then sort the grouped field.
Groups in Tableau are used to manually combine members of a dimension into higher-level categories. For example, you might group several states into a region. While this can simplify a view or reduce the number of members in a dimension, it is not a sorting mechanism. Grouping changes the data structure by altering categories but doesn't provide dynamic control over sorting based on a measure like Sales. Also, once dimensions are grouped, the ability to sort within the original granular levels is lost, defeating the purpose of sorting "across multiple dimensions." Using groups might even mask useful variations in data that you'd want to highlight with a proper sort.

❌ Option D: Right-click on each dimension, choose Sort, then select data source order in the options.
Sorting each dimension individually and selecting data source order simply preserves the original order from the data source, which is not necessarily meaningful or useful for analytical sorting. This method does not allow for sorting based on any measure (such as Sales), nor does it consider the hierarchical relationships between dimensions. If you're dealing with multi-level data and want to see, for example, the highest-grossing Sub-Categories within each Category and Region, sorting by data source order on each field will not produce that result. Instead, it may give a confusing or inconsistent view of the data because it ignores the context needed for nested sorting.

📘 Summary:
To sort data across multiple dimensions based on a measure like Sales, you must use nested sorting on the rightmost dimension in the visual hierarchy. This ensures the sort respects the structure and context of the data. Sets and Groups are used for categorization and filtering—not sorting—and data source order does not reflect analytical priorities.

🔗 Reference:
ℹ️ Tableau Official Docs – Sorting Data
ℹ️ Tableau Video – Sorting Data

Explanation:

To create a histogram on the Quantity field by using bin size of 3, you need to do the following steps:

➡️ Open the link to Book1 found on the desktop. This will open the Tableau workbook that uses the Superstore data source.

➡️ Click on the Histogram tab at the bottom of the workbook to open the Histogram worksheet. You will see a blank worksheet with no marks.

➡️ Right-click on Quantity in the Measures pane and select Create Bins from the menu. This will open a dialog box that allows you to create bins for the Quantity field. Bins are groups of values that are treated as one unit in a histogram.

➡️ Enter 3 in the Size of bins text box. This will set the bin size to 3, which means that each bin will contain values that are 3 units apart. For example, one bin will contain values from 0 to 2, another bin will contain values from 3 to 5, and so on.

➡️ Click OK to create the bins. You will see a new field named Quantity (bin) in the Measures pane with a # sign next to it.

➡️ Drag Quantity (bin) from the Measures pane to Columns on the worksheet. This will create a histogram that shows the distribution of Quantity by bins. You will see bars that represent the frequency or count of values in each bin.

Optionally, you can adjust the width, color, and labels of the bars by using the options on the Marks card. You can also add filters, tooltips, or annotations to enhance your histogram.

🔗 Reference:
➡️ Create Bins from a Continuous Measures

Explanation:

In Tableau Server or Tableau Cloud, when you are subscribed to views (e.g., workbooks, dashboards, or reports) and wish to unsubscribe, you manage your subscriptions through the My Content area of the Tableau web interface. This section allows you to view and manage all content you own or are subscribed to, including the ability to unsubscribe from views that send you email notifications or scheduled updates.

Here’s how it works:

➡️ Navigate to the My Content page on Tableau Server or Tableau Cloud.
➡️ Locate the Subscriptions section, which lists all the views you are subscribed to.
➡️ Select the view(s) you want to unsubscribe from and choose the option to unsubscribe, typically by clicking an unsubscribe link or button next to the subscription.

This process ensures you no longer receive notifications or scheduled snapshots for those views.

❌ Why the other options are incorrect:

B. The Notifications area of Tableau Prep: Tableau Prep is used for data preparation and transformation, not for managing subscriptions to views. It does not have a notifications area for managing view subscriptions, making this option incorrect.

C. The Data Source page of Tableau Desktop: The Data Source page in Tableau Desktop is used for connecting to and configuring data sources, not for managing subscriptions to views published on Tableau Server or Cloud.

D. The Shared with Me page: The Shared with Me page displays content that others have shared with you, but it is not the primary location for managing subscriptions. While you might see shared views, the unsubscribe action is performed in the My Content area under the subscriptions section.

🔗 Reference:
➡️ Tableau Help Documentation: The Tableau Server/Cloud Help page on “Manage Subscriptions” explicitly states that users can manage their subscriptions (including unsubscribing from views) in the My Content area of the web interface.

➡️ Salesforce Tableau Data Analyst Exam Guide: The “Publish and Manage Content” section (10% of the exam) covers managing content, including subscriptions, on Tableau Server or Cloud. It emphasizes using the My Content area for tasks like unsubscribing from views.

➡️ Trailhead Module: The Tableau Data Analyst Certification Prep Guide (Unit 4: Publish and Manage Content) includes scenarios on managing subscriptions, confirming that the My Content area is used for unsubscribing from views.

Explanation:

To create a new calculated dimension field named Category in Tableau that assigns "Sciences" to subjects "Computer Science" or "Science" and "Humanities" to subjects "English" or "Social Studies," you need logical functions that accurately evaluate these conditions. Let’s analyze each option to determine which two achieve the goal.

Option A:
This uses the IF function with multiple ELSEIF clauses to check the value of the [Subject] field. If [Subject] is "Science" or "Computer Science," it assigns "Sciences." If [Subject] is "English" or "Social Studies," it assigns "Humanities." The logic is clear, precise, and covers all specified conditions correctly.
Why it’s correct: It explicitly checks each subject and assigns the appropriate category, meeting all requirements.
Note: The syntax in the question has a minor typo ([Subject]- instead of [Subject]= for the first condition), but assuming this is a formatting error, the logic is valid.

Option B:
The IIF function evaluates a condition and returns one value if true and another if false. Here, it checks if [Subject] contains the string "Science." If true, it assigns "Humanities"; if false, it assigns "Sciences." This logic is incorrect because:
For "Science" or "Computer Science," it would return "Humanities" (since CONTAINS finds "Science" in both), which contradicts the requirement to assign "Sciences."
For "English" or "Social Studies," it would return "Sciences," which is also incorrect.

Why it’s incorrect: The output is reversed, and CONTAINS does not distinguish between "Science" and "Computer Science" accurately, leading to wrong categorizations.

Option C:
The ENDSWITH function checks if [Subject] ends with "Computer Science." If true, it assigns "Sciences"; otherwise, it assigns "Humanities." This is incorrect because:
It only correctly assigns "Sciences" to "Computer Science."
For "Science," it would assign "Humanities" (since "Science" does not end with "Computer Science"), which violates the requirement.
For "English" and "Social Studies," it correctly assigns "Humanities," but it fails to handle "Science" properly.

Why it’s incorrect: It does not account for "Science" as part of the "Sciences" category, missing one of the required conditions.

Option D:
The CASE function evaluates [Subject] and assigns a value based on exact matches. It assigns "Sciences" to "Computer Science" and "Science," and "Humanities" to "English" and "Social Studies." This directly matches the requirements and is a clean, efficient way to handle categorical assignments.
Why it’s correct: It explicitly maps each subject to the correct category, covering all conditions accurately.

Why A and D are the correct choices:

Both A (IF/ELSEIF) and D (CASE) explicitly evaluate each subject and assign the correct category ("Sciences" for "Computer Science" and "Science," "Humanities" for "English" and "Social Studies").
They are precise and handle all specified cases without ambiguity.
The IF function in Option A is more verbose but achieves the same result as the CASE function in Option D, which is more concise for this type of categorical mapping.

References:
1. Tableau Help Documentation:
🧩 IF Function: Tableau’s documentation on logical functions explains that IF and ELSEIF are used to evaluate conditions and return values based on those conditions (Tableau Help: Logical Functions).
🧩 CASE Function: The CASE function is described as a shorthand for multiple IF statements, ideal for mapping specific field values to outputs (Tableau Help: Logical Functions).

2. Salesforce Tableau Data Analyst Exam Guide: The “Explore and Analyze Data” section (41% of the exam) includes creating calculated fields using logical functions like IF and CASE to transform data for analysis.

3. Trailhead Module: The Tableau Data Analyst Certification Prep Guide (Unit 2: Explore and Analyze Data) covers creating calculated fields with IF and CASE functions, with examples of categorizing data based on conditions.

Explanation:

When you create a subscription in Tableau, you are scheduling a view or dashboard to be automatically sent to one or more users on a regular basis. These subscriptions can be delivered via email, and the content is sent in a non-editable, snapshot format that ensures users see the same version of the data, regardless of their Tableau access level.
As of current Tableau Server and Tableau Cloud functionality:

✅ A. PDF
Tableau supports sending dashboards or views as PDF attachments in subscriptions. This is commonly used for sharing static, printable reports. The PDF preserves formatting and is widely accessible.
➡️ Ideal for executives or stakeholders who prefer printable versions.
➡️ Subscription setup allows choosing PDF as the output format.

🔗 Reference:
Tableau Docs – Subscribe to Views
“When you subscribe to a view, Tableau can send you an image or a PDF of that view.”

✅ B. Image
You can also subscribe users to receive an inline image (PNG) of the view in the email body. This allows users to get a quick glance at the dashboard without opening any attachments.
➡️ Very useful for quick updates.
➡️ The image is generated based on the view as it looks at the time the subscription is triggered.

❌ C. Microsoft Excel
Excel export is not supported as a subscription format. While users can manually export data from a view into Excel (using "Export Crosstab"), this cannot be automated through subscriptions. Excel export is reserved for interactive sessions and on-demand use, not for scheduled delivery.

❌ D. Microsoft PowerPoint
PowerPoint is not a supported subscription format either. While Tableau supports exporting dashboards to PowerPoint manually (via the “Export as PowerPoint” option), it does not allow scheduled delivery of dashboards in PowerPoint format through subscriptions.

Explanation:

In this scenario, you are working with two incomplete tables, each containing partial data about books in a library. Since some books exist only in one table and not in the other, and your goal is to retain all data from both tables (no matter where the data resides), you need to use a join that captures everything.
A full outer join is the only join type that ensures:

➡️ All rows from Table A are included
➡️ All rows from Table B are included
➡️ When there's a match on a key (e.g., Book ID), data from both tables is combined
➡️ When there’s no match, rows from one table are still included, with null values for the missing columns from the other

This guarantees nothing is excluded—making it perfect when you’re combining incomplete datasets and need full coverage.

❌ Why other options are incorrect:

B. Right join: Only retains all rows from the right table, and matching rows from the left table. If the left table has unique records, they will be lost.

C. Left join: Retains all rows from the left table, and matching rows from the right. Records unique to the right table are excluded.

D. Inner join: Only includes rows where there is a match in both tables. Any unmatched rows in either table will be dropped—not acceptable if you want full data retention.

📘 Tableau Reference:
"A full outer join combines the results of both left and right outer joins. All records from both tables are included in the result set, with matches where possible and NULLs where no match is found."
🔗 Tableau Docs – Join Types

Explanation:

To determine which two sets in a Tableau workbook can be combined, we need to understand Tableau’s set combination functionality and the context of the given sets: Top N Customers, Customers of 2020, Top N Products, and Sellers of 2020. In Tableau, sets can only be combined if they are based on the same dimension. Combining sets creates a new set that represents the intersection, union, or difference of the members in the original sets. Let’s analyze the sets and options.

Understanding the Sets:

➡️ Top N Customers: Likely based on the Customer dimension, containing a subset of customers (e.g., top N by sales, orders, or another measure).
➡️ Customers of 2020: Also likely based on the Customer dimension, containing customers who made purchases or had activity in the year 2020.
➡️ Top N Products: Likely based on the Product dimension, containing a subset of products (e.g., top N by sales or quantity).
➡️ Sellers of 2020: Likely based on a Seller or Salesperson dimension, containing sellers who were active or made sales in 2020.

The key requirement for combining sets in Tableau is that both sets must be derived from the same dimension. For example, two sets based on the Customer dimension can be combined, but a set based on Customer cannot be combined with a set based on Product.

Analyzing the Options:
➡️ A. Sellers of 2020: Based on the Seller dimension.
➡️ B. Customers of 2020: Based on the Customer dimension.
➡️ C. Top N Products: Based on the Product dimension.
➡️ D. Top N Customers: Based on the Customer dimension.

To choose two sets that can be combined:
B (Customers of 2020) and D (Top N Customers) can be combined because both are based on the Customer dimension. For example, you could create a new set representing the intersection (customers who are both in the top N and active in 2020) or union (all customers who are either in the top N or active in 2020).

Other combinations fail because they involve different dimensions:
➡️ A (Sellers of 2020) and B (Customers of 2020): Cannot combine because Seller and Customer are different dimensions.
➡️ A (Sellers of 2020) and C (Top N Products): Cannot combine because Seller and Product are different dimensions.
➡️ A (Sellers of 2020) and D (Top N Customers): Cannot combine because Seller and Customer are different dimensions.
➡️ B (Customers of 2020) and C (Top N Products): Cannot combine because Customer and Product are different dimensions.
➡️ C (Top N Products) and D (Top N Customers): Cannot combine because Product and Customer are different dimensions.

Context of the Visualization:
The question mentions adding a comment to “March 2020” in a visualization, which suggests a time-based analysis (e.g., a time series chart). However, the task of combining sets is independent of the visualization’s time component. The sets are defined by dimensions (Customer, Product, Seller), and the combination logic depends solely on whether the sets share the same dimension.

Why B and D are Correct:
➡️ Customers of 2020 and Top N Customers are both based on the Customer dimension, allowing Tableau to combine them using operations like intersection, union, or difference.
➡️ Combining these sets could, for example, identify customers who are both in the top N by sales and active in 2020, which might be useful for annotating a visualization (e.g., highlighting key customers in March 2020).

References:
1. Tableau Help Documentation:
➡️ Sets in Tableau: The documentation explains that sets can be combined only if they are based on the same dimension. Combining sets creates a new set using operations like union or intersection.
➡️ Combine Sets: Specific guidance on combining sets notes that both sets must reference the same dimension for the operation to be valid (Tableau Help: Combine Sets).

2. Salesforce Tableau Data Analyst Exam Guide: The “Explore and Analyze Data” section (41% of the exam) covers creating and using sets, including combining sets to filter or segment data.

3. Trailhead Module: The Tableau Data Analyst Certification Prep Guide (Unit 2: Explore and Analyze Data) includes scenarios on creating and combining sets, emphasizing that sets must share the same dimension for combination.

Explanation:

To add a specific comment to a data point—like March 2020 on a line chart—you use annotations in Tableau. An annotation allows you to call out important events, outliers, or milestones directly on the chart. In this case, you might want to highlight something significant that happened in March 2020 (e.g., a drop in sales due to COVID-19).

To do this:

➡️ Right-click on the data point for March 2020 in the line chart.
➡️ Select "Annotate" → choose "Mark", "Point", or "Area" depending on the context.
➡️ Enter your comment in the annotation box that appears.

This will visually anchor the note to that specific part of the chart, making it clear and informative for viewers.

❌ Why the other options are incorrect:

A. Drag the growth rate to Text on the Marks card: This would display numerical values as labels on the chart, not explanatory comments. It’s for data labeling, not annotation.

B. Enable captions: Captions add a description below the chart for the whole worksheet—not for a specific data point. This wouldn’t help you comment on just March 2020.

D. Add a tooltip: Tooltips appear on hover, not as visible comments on the chart. Also, they don’t persist unless the user interacts with the chart.

📘 Tableau Reference:
"You can annotate a point, an area, or a mark to help explain data in your view."
🔗 Tableau Docs – Annotate Views

Explanation:

To calculate the grand total of all regions in Tableau using a Level of Detail (LOD) expression, you need an expression that sums the Sales across all regions without being restricted by any dimensions in the view, such as [Region]. LOD expressions in Tableau allow you to control the level of granularity for calculations. Let’s analyze the requirement and each option.

Requirement:
The goal is to compute the grand total of Sales for all regions in the dataset, regardless of how the data is filtered or displayed in the view (e.g., even if the view is broken down by region or other dimensions).
This implies a calculation that ignores dimensions like [Region] and returns a single total value for all Sales.

Understanding LOD Expressions:
FIXED: Computes a value at a specified level of detail, ignoring all dimensions in the view except those explicitly included in the LOD expression. If no dimensions are specified, it computes the value across the entire dataset.
INCLUDE/EXCLUDE: Adjusts the level of detail relative to the view, but these are not relevant here since the options only use FIXED.
SUM: Aggregates the specified measure (e.g., Sales).
TOTAL: A table calculation function, not an LOD function, and is not used within FIXED LOD expressions.

Analyzing the Options:

A. {FIXED: [Region] SUM Sales}
Interpretation: This is syntactically incorrect. The correct syntax for a FIXED LOD is {FIXED [Dimension]: Aggregation}. The expression should be {FIXED [Region]: SUM(Sales)}. If corrected to {FIXED [Region]: SUM(Sales)}: This calculates the total Sales for each [Region], not the grand total across all regions. It fixes the calculation at the [Region] level, so you get a sum of Sales per region, not a single grand total.
Why incorrect: It does not compute the grand total of all regions; it computes region-level totals.

B. {FIXED: SUM(Sales)}
Interpretation: This expression calculates the sum of Sales across the entire dataset, ignoring all dimensions in the view (including [Region]). Since no dimensions are specified in the FIXED LOD, it operates at the dataset level, returning a single grand total. Why correct: This matches the requirement to calculate the grand total of Sales for all regions, as it sums all Sales values without partitioning by any dimension.

C. {FIXED: [Region]: TOTAL Sales}
Interpretation: This is syntactically incorrect. The TOTAL function is a table calculation, not a valid aggregation within an LOD expression. LOD expressions use aggregations like SUM, AVG, etc., not TOTAL. Why incorrect: The syntax is invalid, and even if it were valid, it would not compute the grand total across all regions correctly.

D. {FIXED: TOTAL(Sales)}
Interpretation: This is also syntactically incorrect. The TOTAL function cannot be used within a FIXED LOD expression, as it is a table calculation, not an aggregation.
Why incorrect: The syntax is invalid, and it does not align with the requirement.

Why Option B is the Best Choice:
The expression {FIXED: SUM(Sales)} computes the grand total of Sales across the entire dataset, ignoring all dimensions (including [Region]). This is exactly what the question asks for: a single value representing the total Sales for all regions.
Example: If the dataset has Sales values for multiple regions (e.g., North: $100, South: $200, East: $300), this expression returns $600, the grand total.

References:
1. Tableau Help Documentation:
Level of Detail Expressions: Explains that {FIXED: SUM([Measure])} computes the measure’s aggregation across the entire dataset, ignoring all dimensions unless specified (Tableau Help: Level of Detail Expressions).
FIXED LOD: Notes that omitting dimensions in a FIXED LOD results in a calculation at the dataset level (Tableau Help: FIXED LOD).

2. Salesforce Tableau Data Analyst Exam Guide: The “Explore and Analyze Data” section (41% of the exam) covers creating calculations, including LOD expressions, to perform aggregations at different levels of granularity.

3. Trailhead Module: The Tableau Data Analyst Certification Prep Guide (Unit 2: Explore and Analyze Data) includes examples of LOD expressions, such as {FIXED: SUM(Sales)}, for calculating grand totals across a dataset.

Explanation:

To add a distribution band on the Profit measure to show the standard deviation from -1 to 1 in the SalesVSProfit worksheet of the Book1 Tableau workbook, follow these steps. This process assumes you are working in Tableau Desktop and that the SalesVSProfit worksheet contains a visualization (e.g., a scatter plot) with Sales and Profit data, likely from the Sample Superstore dataset.

Steps to Add a Distribution Band:

1. Open the Workbook:
Locate and open the Book1 link on your desktop. This will launch Tableau Desktop and load the Book1 workbook.

2. Navigate to the SalesVSProfit Worksheet:
At the bottom of the Tableau workbook, click the SalesVSProfit tab to open the worksheet. You should see a visualization, such as a scatter plot, showing the relationship between Sales and Profit for a dimension (e.g., Sub-Category).

3. Access the Analytics Pane:
➡️ On the left side of the Tableau Desktop interface, locate the Analytics tab (next to the Data pane).
➡️ Click the Analytics tab to open the Analytics pane, which lists analytical objects like Reference Line, Distribution Band, and Box Plot.

4. Add the Distribution Band:
➡️ In the Analytics pane, find Distribution Band under the Model section.
➡️ Drag Distribution Band from the Analytics pane and drop it onto the Profit measure on the Rows shelf (or the axis where Profit is displayed, typically the vertical axis in a scatter plot).
➡️ Tableau will display a dialog box titled Edit Reference Line, Band, or Box.

5. Configure the Distribution Band:
In the Edit Reference Line, Band, or Box dialog box:

➡️ Scope: Select Table, Pane, or Cell depending on the desired scope. For a scatter plot with Sub-Category on the Columns shelf, Table is typically appropriate to compute the standard deviation across all data points in the view.
➡️ Value: Choose Standard Deviation from the dropdown menu.
➡️ Factors: Set the Band From value to -1 and the Band To value to 1. This configures the band to show one standard deviation below and above the mean of Profit.
➡️ Label: Choose an option for labeling (e.g., Value to show the standard deviation values, Computation to show the field name, or Custom for a custom label). For clarity, Value is often used.
➡️ Formatting: Adjust line styles, colors, or shading as needed (e.g., select a fill color for the band to make it visible).

Click OK to apply the changes.

6. Verify the Result:
The visualization will now display a shaded band on the Profit axis, representing the range from -1 to +1 standard deviations from the mean of Profit. This band highlights the variability of Profit values, with approximately 68% of the data (assuming a normal distribution) falling within this range.

Explanation:
➡️ What the Distribution Band Does: The distribution band visualizes the spread of Profit values around the mean, with the -1 to 1 standard deviation range indicating where most data points lie in a normal distribution. In a scatter plot, this band appears as a shaded area along the Profit axis, helping to identify outliers or typical profit ranges.

➡️ Scope Considerations:
Table: Computes the standard deviation across all data in the view (e.g., all Sub-Categories).
Pane: Computes the standard deviation for each pane (e.g., each Sub-Category if Sub-Category is on the Columns shelf).
Cell: Computes the standard deviation for each mark (less common for this scenario).

For a grand total across all regions or categories, Table is typically the correct scope, but adjust based on the visualization’s structure.

➡️ Standard Deviation: The band from -1 to 1 standard deviations covers approximately 68% of the data in a normal distribution, making it a common choice for visualizing data variability.

Notes:
➡️ If the SalesVSProfit worksheet is a scatter plot with Sub-Category on Columns, Profit on Rows, and Sales on Columns or another shelf, the distribution band will apply to the Profit axis across all Sub-Categories (if Table scope is selected).
➡️ If the visualization includes filters or other dimensions, ensure they don’t restrict the data in a way that affects the standard deviation calculation.
➡️ The question about adding a comment to March 2020 from your previous query is unrelated to this task, as distribution bands are about visualizing data spread, not adding comments. If you need to add a comment or annotation, that would involve right-clicking a data point for March 2020 and selecting Annotate > Mark or Annotate > Point, but this is not part of the current question.