Analytics-DA-201 Salesforce Certified Tableau Data Analyst Questions and Answers

When you sign in to a Tableau Server site from Tableau Desktop and search for a published data source, you will see all the data sources published to the site within the folders to which you have access. You will not see data sources published to other sites or folders that you do not have permission to view. You can also filter your search by project, owner, or tag. References: https://help.tableau.com/current/pro/desktop/en-us/connect_basic.htm https://help.tableau.com/current/pro/desktop/en-us/connect_tableauserver.htm

When signed in to Tableau Server from Tableau Desktop, you ' ll only be able to see the data sources that have been published to the site and are within the folders that you have been granted access to. Tableau ' s permission and project structure mean that you won ' t see all data sources from the entire server unless you have permissions to do so.

To combine the tables and meet the requirements, you should use a left join. A left join will keep all the records from the left table (Employeelnfo) and match them with the records from the right table (DepartmentInfo) based on the common field (Department ID). If there is no matching record in the right table, the fields from the right table will be null. This way, you will retain all the records from Employeelnfo, and also include the Department Name, Size, and VP fields from DepartmentInfo. Every record will have a full name because it is a field from the left table. A left join will look like this:

To analyze the average revenue per product in different regions over time, you need to use two fields that contain numeric, quantitative values that you can measure and aggregate. Quantity and Revenue are both measures that fit this criterion. You can multiply Quantity and Revenue to get the total sales for each product, and then divide by the number of products to get the average revenue. You can also use these measures to create charts and tables that show the trends and comparisons over time and across regions. References:

Dimensions and Measures, Blue and Green - Tableau

Tableau Certified Data Analyst Study Guide

A Parameter action is designed to let users change a parameter value through direct interaction with a visualization. The parameter can then drive a calculated field that determines which measure—such as Sales, Profit, Quantity, or another metric—is displayed on the chart. Tableau ' s current action framework explicitly includes Change Parameter as an action type and notes that parameter actions can be used with calculations to customize how data is displayed. A Set action changes membership in a set, a Highlight action emphasizes selected marks without changing the metric being displayed, and a Filter action restricts the data shown in the target view. Because the requirement is to let the user dynamically select the measure displayed rather than filter or highlight records, the appropriate action type is Parameter .

To show the corresponding region when users hover their mouse over any of the bars, you need to drag Region to Tooltip on the Marks card. This will add Region as a field in the tooltip text that appears when users hover over a mark. You can also customize the tooltip text by editing it in the Tooltip dialog box. References: https://help.tableau.com/current/pro/desktop/en- us/buildmanual_shelves.htm https://help.tableau.com/current/pro/desktop/en-us/formatting_tooltips.htm

To show additional information such as the corresponding region when hovering over a bar in Tableau, you can drag the ' Region ' field to the ' Tooltip ' shelf on the ' Marks ' card. This will ensure that when a user hovers over a bar representing a city, the tooltip will dynamically display the region associated with that city.

For a published Tableau data source, the Connect capability controls whether a user can connect to that data source from Tableau Desktop and use it for analysis. Tableau permissions also determine whether the content is visible within the site and project hierarchy, but among the choices, Connect is the capability specifically associated with making a usable published data source available for connection. Certification does not restrict the list to certified sources only, and ownership is not required; users can connect to data sources created by other publishers when permissions allow it. A user’s home directory is not the governing scope for published Tableau data sources. Therefore, the correct choice is the data sources for which the user has the required Connect permission, subject to normal site and content visibility permissions.

The target visualization shows a subtotal after the members of each Category while retaining the individual Sub-Category rows. In Tableau, Analysis > Totals > Add All Subtotals inserts subtotal rows or columns when multiple dimensions are present on Rows or Columns. Here, Category and Sub-Category are both on Rows, so Tableau can calculate a subtotal for each Category group. Show Column Grand Totals and Show Row Grand Totals create totals for the entire view along the corresponding direction; they do not insert a separate total inside every Category section. Column Totals to Top only changes the placement of existing column totals. Because the requirement is to add a Total row for Furniture, Office Supplies, and Technology individually, Add All Subtotals is the correct command.

To configure the shapes to be much larger than the line, you need to duplicate Population and drag it to the second Marks card. This will create a dual axis chart with two measures on one axis. You can then select Shape on one Marks card and Line on another Marks card, and adjust the size of each mark independently using the Size slider or menu. References: https://help.tableau.com/current/pro/desktop/en-us/multiplemeasures_dualaxes.htm https://help.tableau.com/current/pro/desktop/en-us/marks_markproperties_size.htm

In a dual-axis chart in Tableau, if you want to have two different visual mark types (like a shape and a line) and configure them differently (such as making one larger than the other), you would need to duplicate the measure. You then drag this duplicate to the second Marks card (which represents the second axis). There, you can adjust the size of the marks (shapes, in this case) independently of the line marks on the first Marks card.

The matching-pattern capability is configured in an Input step in Tableau Prep Builder. When connecting to file-based data such as text files or Excel files, Tableau Prep can include multiple files and create an input union by using wildcard or matching-pattern criteria. This allows similarly named files to be combined into a single table as they are brought into the flow. A separate Union step combines already-added tables or inputs, but the question specifically asks where a single table can be created from a matching pattern of multiple file names. Join combines tables based on key fields, while New Rows generates missing sequential records. Because wildcard file matching is part of the file-ingestion configuration, Input is the correct step.

A scatter plot requires two quantitative measures on opposing axes, so Math Score should be placed on Rows and History Score on Columns. To produce one mark for each student rather than a single aggregated mark, Student ID should be placed on Detail on the Marks card. The exhibit also contains a linear model line, which is created by dragging Trend Line from the Analytics pane onto the view. A reference line is different: it marks a fixed or computed value on an axis and does not model the relationship between two measures. Adding both score fields to the Marks card is also unnecessary because their positions are already defined by the Rows and Columns shelves. These steps follow Tableau’s standard scatter-plot and trend-line workflow.

To add a comment to March 2020 on a line chart, you need to annotate that mark. You can right-click on the mark and select Annotate > Mark from the menu. This will open a dialog box where you can type your comment and format it as you like. You can also drag and drop the annotation to position it on the worksheet. Dragging the growth rate to Text on the Marks card will add a label to every mark on the line chart, enabling captions will show a description of the worksheet at the bottom, and adding a tooltip will show a text box when users hover over a mark. References: https://help.tableau.com/current/pro/desktop/en-us/formatting_annotations.htm https://help.tableau.com/current/pro/desktop/en-us/formatting_annotations_create.htm

To add a comment to a specific point on a line chart, such as March 2020 in the provided visualization, you would use the ' Annotate ' feature. Right-clicking on the data point for March 2020 and selecting ' Annotate ' allows you to add a text annotation directly to the chart, which can be used to highlight and comment on that particular data point.

For a published data source used by multiple viewers and explorers, row-level security should be enforced centrally at the data-source level. Tableau supports user-based security calculations that identify the signed-in user and return only the rows that user is entitled to see. Placing the security logic in a data source filter before publishing ensures that every workbook connected to that published source receives the restricted data. Tableau also prevents downstream authors from seeing or modifying those data source filters, which strengthens consistency across consuming workbooks. A normal dimension filter in an individual worksheet is easier to change and is not a centralized RLS control. Data source permissions control access to the published asset itself, not which rows an authorized user can see. Therefore, option C best implements the stated row-level security requirement.

we need to create a calculated field that displays the count of returns only for the first year in the table. To do this, we can use the following formula:

IF FIRST () = 0 THEN COUNT (Returns) END

This formula uses the FIRST () function, which returns the number of rows from the current row to the first row in the partition. If the current row is the first row, then FIRST () returns 0. The IF statement checks if this condition is true, and if so, it returns the count of returns for that row. Otherwise, it returns NULL.

To complete the formula in the calculated field, you should select the following options from the drop-down menus:

FIRST ()

0

COUNT (Returns)

https://help.tableau.com/current/pro/desktop/en-us/functions_functions_date.htm

A data source filter is a filter that applies to all the worksheets that use the same data source. It filters the data before any other filters or calculations are applied. You can add a data source filter from the Data Source page by clicking on the Add button next to Filters.

The type of filter that will appear in the Edit Data Source Filters dialog box is a dimension filter on all the sheets. This means that you can choose a dimension from your data source and filter it by values, range, condition, or top/bottom. The filter will affect all the worksheets that use that dimension.

The other types of filters are not data source filters and will not appear in the Edit Data Source Filters dialog box. A table calculation filter is a filter that applies to a table calculation, such as percent of total or running total. A top N condition filter is a filter that shows only the top or bottom N values of a measure or dimension based on a condition. A context filter is a filter that creates a subset of data that other filters can use.

To connect to a tab-delimited data file named sales.tsv, you should select the option To a File > Text file in Tableau Desktop. This option will allow you to browse and select the file from your local or network drive and connect to it as a data source. You can then specify the text file properties, such as the delimiter, the text qualifier, and whether the first row contains column names. You can also join, union, or blend the data with other data sources if needed. References:

Text File - Tableau

Tableau Certified Data Analyst Study Guide

Common Characters: Find and group values that have letters or numbers in common. This option uses the ngram fingerprint algorithm that indexes words by their unique characters after removing punctuation, duplicates, and whitespace. This algorithm works for any supported language. This option isn ' t available for data roles. For example, this algorithm would match names that are represented as " John Smith " and " Smith, John " because they both generate the key " hijmnost " . Since this algorithm doesn ' t consider pronunciation, the value " Tom Jhinois " would have the same key " hijmnost " and would also be included in the group.

Set actions allow selections made in a worksheet or dashboard to update membership in an existing set. That set can then be used in calculations or placed on the Marks card to change how another visualization is encoded. A common design is to place set membership on Color, so selecting marks in a source view changes which marks are in or out of the set and therefore changes their colors in a target view. This is exactly the interaction described in option B. Set actions are not intended to join or filter across unrelated data sources automatically, and pie-chart dataset-size comparison is simply a chart-design task. Likewise, highlighting proportions in a Gantt chart is not a defining set-action behavior. The key mechanism is selection-driven set membership that controls a visual property such as Color.

Common Characters is appropriate because the five values contain essentially the same characters but differ in capitalization, punctuation, separators, and—in one case—the order of the first and last names. Tableau Prep’s Common Characters fuzzy-matching algorithm removes punctuation, duplicates, and whitespace before evaluating the character fingerprint. Tableau specifically documents that values such as “John Smith” and “Smith, John” can be matched by this technique because their characters are equivalent despite being reordered. Pronunciation is intended for values that sound alike, while Spelling uses edit-distance similarity and is better suited to typographical variations. Manual Selection would require the analyst to choose the values explicitly instead of grouping them automatically. Therefore, B is the verified answer.

Tableau Prep provides a New Rows step specifically for filling gaps in sequential numeric or date data. When a dataset should contain one record per day but some dates are absent, New Rows can generate the missing date records between existing dates or within a defined range. The analyst can set the increment to one day and choose how other fields in the generated records are populated, such as Null, zero, or values copied from the previous row. Union combines rows from separate tables, Pivot reshapes columns and rows, and Aggregate changes the level of detail by grouping records. None of those operations is designed to manufacture missing dates in a sequence. Therefore, New Rows is the correct Tableau Prep operation for this requirement.

To combine the tables by using Tableau Prep and include only values from Table1 that do not match any values in Table2, you should use a left only join. A left only join is a type of join that returns only the rows from the primary table (Table1) that have no matching rows in the secondary table (Table2). The field values from Table2 will appear as null values in the combined table. A left only join is also known as an anti-join or an exclusion join. References:

Tableau Certified Data Analyst Exam Prep Guide , page 9, section “Preparing Data”

Tableau Help: Join Your Data

Tableau Help: Left Only Join

A correct year-to-date filter must satisfy two requirements: the event must belong to the current calendar year, and its date must not be later than today. DATETRUNC( " year " , [Event Date]) = DATETRUNC( " year " , TODAY()) confirms that the event and today’s date fall within the same year. [Event Date] < = TODAY() then excludes future dates within that year. Option C identifies the current year but would also include future-dated events. Option D does the opposite by explicitly selecting dates after today. Option B compares each event date with the first day of its own year and therefore does not implement YTD filtering. Tableau documents DATETRUNC as truncating a date to a specified date-part level and TODAY() as returning the current date.

The requirement is based on total sales by store, so the filter must operate on the Store Number dimension while evaluating an aggregated Sales condition. Tableau dimension filters provide a Condition tab that can retain dimension members according to an aggregate measure, for example SUM([Sales]) > = 500. Applying that logic as an extract filter ensures that only qualifying store members are included in the resulting extract. Filtering the Sales measure by an “at least” row-level value would incorrectly evaluate individual records rather than each store’s total. A Top filter ranks members and is intended for top or bottom N selection, not for a fixed minimum total. Therefore, the analyst should filter Store Number by a condition based on Sales, which corresponds to option B.

To use the City dimension to create a dynamic filter that shows the cities that have a population greater than one million, you should use a condition filter. A condition filter is a type of filter that shows only the values that meet a specified condition based on a measure or a calculation. You can create a condition filter by dragging a dimension to the Filters shelf and selecting Condition from the dialog box. Then you can enter a formula or choose an option that defines your condition.

In this case, you want to create a condition filter based on Population, which is a measure. You can drag City to the Filters shelf and select Condition from the dialog box. Then you can choose By field from the tab and select Population > Sum > Greater than > 1000000 from the drop-down lists. This will create a condition filter that shows only the cities that have a sum of population greater than one million.

The other options are not correct for this scenario. A general filter is not a specific type of filter, but a term that refers to any type of filter in Tableau. A wildcard filter is a type of filter that shows only the values that match a specified pattern or string, such as “New*” or “*ton”. A top filter is a type of filter that shows only the top or bottom N values of a measure or dimension based on a ranking or an aggregation. References: https://help.tableau.com/current/pro/desktop/en-us/filtering.htm https://help.tableau.com/current/pro/desktop/en-us/filtering_condition.htm https://help.tableau.com/current/pro/desktop/en-us/filtering_wildcard.htm https://help.tableau.com/current/pro/desktop/en-us/filtering_topn.htm

The chart is partitioned into separate panes by year, and each pane contains its own horizontal reference line based on the values within that pane. Therefore, the reference-line Scope must be set to Pane , which instructs Tableau to calculate and draw a separate line for each pane rather than a single line across the entire table. The line also represents a computed mean rather than a fixed numeric threshold, so the aggregation must be Average . Tableau ' s official documentation states that Table, Pane, and Cell determine the scope of a reference line, while Average places the line at the average value along the applicable axis. A Table-scoped average would produce one overall line, and a Constant would use a manually specified value rather than recalculating per pane.

The box plot visualization displays the distribution of average profits by region. The distribution of profits is represented by the range of the box (interquartile range) and the whiskers (which often represent the total range excluding outliers). From the image provided, the South region has the smallest visible range within its box and the shortest whiskers, indicating the smallest distribution of average profits among the choices provided.

Replace Data Source

Replace Data Source is a feature that allows you to replace one data source with another one that has the same or similar fields and structure. It updates all the worksheets that use the original data source with the new one, without reloading the data. In this case, you want to replace the data source that contains order data from 2019 with the one that contains order data from 2020.

To replace the data source, you need to do the following steps:

Go to the Data Source tab at the bottom of the workbook to see the data source page. You will see a table that shows the fields and values from the original data source.

Click on the drop-down arrow next to the data source name at the top of the page and select Replace Data Source from the menu. This will open a dialog box that allows you to replace your data source with another one.

Select the data source that contains order data from 2020 from the list of available data sources and click OK. This will replace your original data source with the new one and update your worksheet accordingly. You may need to fix any broken fields or calculations that result from the replacement.

A URL action is Tableau’s mechanism for opening an external web page or web-based resource from a worksheet or dashboard. Because the requirement says the page must open when the user clicks the sheet, the action should be configured as Go to URL with Run action on set to Select. Select runs the action when the user clicks a mark, while Menu requires the user to open a context or tooltip menu and choose the action explicitly. A navigation button and a Go to Sheet action move between Tableau sheets or dashboards rather than opening an external webpage. A Web Page object embeds web content within a dashboard and does not satisfy the requirement to open the specific page in the default browser when the sheet is clicked. Therefore, option C is the appropriate configuration.

To have each record alternate between grey and white, you should change the row banding size to 1 in the Format Shading pane. This setting will ensure that each row has a different shading from the one above and below it, creating an alternating pattern of grey and white rows. References: The answer can be verified through practical application within Tableau or by consulting Tableau’s official learning resources, such as  Tableau Help: Format Shading  or  Tableau Training Pass: Formatting .

The question presents a scenario where a geographic map visualization in Tableau needs to be transformed from a series of discrete circles representing data points to a density map visualization. The density map shows concentrations of data points with a gradient of color, where denser areas are indicated by a darker color.

Here ' s the explanation for each option:

A. Change the mark type to Density: This is the correct answer because changing the mark type to ' Density ' in Tableau creates a density map, which displays the concentration of data points with a color gradient. This is exactly what is needed to achieve the visual effect shown in the second image, where regions with a higher concentration of data points are represented by darker shades.

B. Drag Location to Size on the Marks card: This option would adjust the size of the marks based on the number of locations, which is not relevant to creating a density map. It would result in varying sizes of circles, not a continuous gradient.

C. Change the mark type to Map: The visualization is already using a map. This option would not change the visualization to the desired density map.

D. Drag Population to Size on the Marks card: This would change the size of the circles based on the population values, making some circles larger and others smaller. This is not how a density map is created, which uses color intensity rather than size to show concentration.

E. Change the opacity to 75%: Changing the opacity would affect the transparency of the marks on the map but would not transform the visualization into a density map.

To achieve the visualization shown in the second image, the mark type must be changed to ' Density, ' which will produce a heat map-like effect where the color intensity represents the concentration of data points. Therefore, the correct answer is A. Change the mark type to Density .

To create a density map from a scatter plot of data points, you would change the mark type to Density. This mark type allows you to visualize the concentration of data points in an area, which can be useful for identifying clusters or patterns in geospatial data.

Data interpreter is a feature that helps you clean and structure your data in Excel before importing it into Tableau. It detects and removes any headers, footers, subtotals, or other elements that are not part of the actual data. It also splits any merged cells and fills in any missing values. In this case, data interpreter provides the following results:

It removes the first three rows and the last two rows that contain headers and footers.

It splits the merged cells in column A and fills in the missing values with “Fiction” or “Non-Fiction”.

It does not remove or change any other rows or cells.

Therefore, the number of rows of data that will be ingested into Tableau as values is 17, which is the number of rows left after removing the headers and footers. References: https://help.tableau.com/current/pro/desktop/en-us/importing_cleaning_up_data.htm https://help.tableau.com/current/pro/desktop/en-us/importing_data_interpreter.htm

Based on the Data Interpreter ' s results and the provided Excel dataset screenshot, the number of rows of data that will be ingested into Tableau as values is 17. The Data Interpreter has identified and excluded headers and footers, and the rows with actual data are considered for analysis.

To connect a Tableau workbook to a dataset in a Microsoft Excel spreadsheet, you need to select New Data Source from the Data menu. This will open the Connect pane, where you can choose Microsoft Excel as your data source and browse for your spreadsheet file. You can then drag and drop your sheets or tables to join or union them in the data source page. References: https://help.tableau.com/current/pro/desktop/en-us/connect_basic.htm https://help.tableau.com/current/pro/desktop/en-us/connect_excel.htm

When connecting to a dataset in Tableau Desktop, you would go to the ' Data ' menu and select ' New Data Source ' . This allows you to connect to various types of data sources, including Microsoft Excel spreadsheets, where you can then select the specific file you wish to connect to.

To combine the two tables that contain data about books in a library and ensure that all the data is retained, you should use a full outer join. A full outer join is a type of join that returns all rows from both tables, regardless of whether there is a match or not. If there is no match, null values are filled in for the missing fields.

To perform a full outer join, you need to do the following steps:

Connect to both tables as your data sources in Tableau. You can use either live or extract connections.

Drag one table to the canvas and drop it on top of another table. This will create a join between them based on a common field.

Click on the join icon between the tables and select Full Outer Join from the drop-down list. This will change the join type to full outer join and show all rows from both tables.

Optionally, you can add or remove join clauses by clicking on Add or Remove buttons next to each clause. You can also change or rename fields by clicking on them.

The other types of joins are not correct for this scenario. An inner join returns only the rows that have a match in both tables, which will exclude any books that are missing from either table. A left join returns all rows from the left table and only the matching rows from the right table, which will exclude any books that are only in the right table. A right join returns all rows from the right table and only the matching rows from the left table, which will exclude any books that are only in the left table. References: https://help.tableau.com/current/pro/desktop/en-us/joining_tables.htm https://help.tableau.com/current/pro/desktop/en-us/join_types.htm

When combining two datasets that are both incomplete and where it ' s important to retain all data from both sources, a full outer join is appropriate. This type of join ensures that all records from both tables are included in the combined dataset, even if there are no matching records in the other table.

Bins divide a continuous numeric measure into a series of equal-sized ranges, making them the correct Tableau feature for analysing a salary distribution. For example, salary values could be divided into intervals of £10,000, with each employee assigned to the corresponding salary bin. The resulting binned dimension can then be used to create a histogram showing the frequency of salaries in each interval. Tableau documentation describes each bin as an equal-sized container representing a defined numeric range. A Group combines selected dimension members manually or logically, but it does not inherently create equal numeric intervals. A hierarchy establishes drill-down relationships between dimensions, while a Set defines a subset of dimension members according to selection or rules. Therefore, Bins directly satisfy the requirement to divide a continuous salary range into equal intervals.

Both IFNULL and ZN can replace null numeric values with zero. IFNULL([Profits], 0) returns the Profit value when it is non-null and explicitly returns 0 when the field is null. ZN([Profits] ) is a specialized null-handling function that returns the original expression when a value exists and zero when it is null. Tableau’s documentation specifically describes ZN as useful for replacing null numeric values with zeros. ISNULL([Profits]) returns a Boolean TRUE/FALSE result rather than a numeric replacement. Option C is also inappropriate because nulls cannot be reliably tested by ordinary equality or inequality comparisons; Tableau supplies ISNULL for that purpose, and the proposed formula does not substitute zero. Therefore, A and D both produce the required numeric result while retaining existing non-null Profit values.

To refresh an extract on Tableau Server, you need to select the workbook, select Refresh Extracts, and then add a new extract refresh. You can specify the frequency and time of the refresh, as well as any custom options. You can have multiple extract refreshes for the same workbook with different schedules. References: https://help.tableau.com/current/server/en-us/refresh_extracts.htm https://help.tableau.com/current/server/en-us/refresh_extracts_add.htm

On Tableau Server, you can manage the scheduling of extract refreshes by selecting the specific workbook and configuring additional refresh schedules as needed. This allows for multiple refresh triggers, such as the regular Monday at 10:00 AM schedule, as well as an additional trigger for the last day of the month at 18:00.

An INCLUDE LOD expression is specifically useful when a calculation must incorporate a finer-grained dimension that is not already present in the visualization. INCLUDE adds the specified dimension to the level of detail established by the dimensions currently in the view, calculates the expression at that finer grain, and then allows Tableau to reaggregate the result at the view’s level. For example, { INCLUDE [Customer Name] : SUM([Sales] ) } can calculate sales per customer even when Customer Name itself is absent from the displayed view. If the dimension is already present in the view, INCLUDE generally adds no additional granularity for that field. The dimension does not specifically need to reside on the Marks card, Rows/Columns shelves, or Filters shelf. Therefore, Not in the View is the correct answer.

A Gantt chart is specifically designed to represent the duration of events or activities along a time axis. Each Gantt mark normally begins at a date or time position, while the mark’s size represents the duration of the activity. This makes the visualization highly appropriate for project schedules, where analysts need to see when individual tasks start and how long each task lasts. Tableau’s official documentation describes Gantt charts as a method for showing event or activity duration and demonstrates their use for elapsed time between dates. Regional performance comparison would normally use bars, maps, or other comparative visualizations. Correlation analysis is better suited to a scatter plot, while long-term budget trends are normally displayed with line charts. Therefore, C is the appropriate Gantt chart use case.

Tableau Desktop supports custom mark shapes by loading image files from the Shapes folder inside My Tableau Repository. The analyst should copy the new shape files into a folder under My Tableau Repository\Shapes. Tableau uses that folder as a custom shape palette, and the analyst can then select the shapes from the Marks card by choosing Shape and editing the shape assignment. Once custom shapes are used, they are stored with the workbook, which supports sharing the visualization with other users. Dragging image files directly into a workbook does not register them as reusable mark shapes, and a URL cannot be used as the workbook’s shape repository. My Tableau Prep Repository is also incorrect because Tableau Prep is not where Tableau Desktop mark-shape palettes are stored.

The LAST() function in Tableau returns the number of rows from the current row to the last row in the partition. When you compute using ' Order Date ' , it will change the partitioning of the calculation. If ' Consumer ' in ' 2018 ' is the last row in its partition when computed by ' Order Date ' , then LAST() will return 0 for that cell.

The LAST() function in Tableau is a table calculation that returns the number of rows from the current row to the last row in the partition. The value of LAST() is 0 for the last row, increases by 1 for each row above the last row, and can be negative for rows below the current row if there are such rows in the partition.

In the provided visualization, LAST() is set to compute using Table (across). Changing the compute mode to use " Order Date " will adjust the partitioning of the data upon which the LAST() function is calculated. Since " Order Date " is likely to be a unique value per row (assuming each order has a unique date), each row becomes its own partition.

For the Consumer segment of the year 2018, if " Order Date " is unique for each row, then the last row in each partition (in this case, each individual row) will have a LAST() value of 0 because there are no other rows in the partition - it ' s the last row of its own partition.

Therefore, when you change the compute mode of LAST() to use " Order Date " , each cell under the Consumer segment for the year 2018 will have the LAST() value of 0, because each order date creates a partition of one, making every row the last in its partition.

To create a new calculated dimension field named Category that meets the given conditions, you can use either the IF or the CASE logical function. Both functions allow you to evaluate an expression and return a value based on different scenarios. Option A uses the IF function with multiple ELSEIF clauses to check the value of the Subject field and assign it to either ‘Sciences’ or ‘Humanities’. Option D uses the CASE function with multiple WHEN clauses to do the same thing. Both options will produce the same result, but the CASE function is more concise and easier to read. Option B is incorrect because it will assign ‘Humanities’ to any subject that contains ‘Science’ in its name, which is not the desired outcome. Option C is incorrect because it will only check if the subject ends with ‘Computer Science’ and ignore the other subjects. References:

Logical Functions - Tableau

Tableau Certified Data Analyst Study Guide

The object that you should add to the dashboard to allow users to switch to a different dashboard in the workbook is Navigation.

Navigation objects are used to create links between dashboards or sheets in a workbook. You can add a navigation object to a dashboard by dragging it from the list of objects and dropping it on the dashboard. You can then configure the navigation object to specify the destination dashboard or sheet that you want users to navigate to when they click on it.

You can learn more about navigation objects and how to use them in this article: https://help.tableau.com/current/pro/desktop/en-us/dashboards_navigation.htm

A .hyper file is an extract file that contains a snapshot of data from a data source. It is a compressed and optimized file format that can be used to improve the performance and portability of dashboards and workbooks. A .twb file is a workbook file that contains the visualization and connection information, but not the data itself. A .csv file is a comma-separated values file that contains plain text data. A .mdb file is a Microsoft Access database file that contains tables, queries, forms, and other objects. References: https://help.tableau.com/current/pro/desktop/en-us/save_savework_packagedworkbooks.htm https://help.tableau.com/current/pro/desktop/en-us/extracting_data.htm https://help.tableau.com/current/pro/desktop/en-us/examples_csv.htm https://support.microsoft.com/en-us/office/introduction-to-access-database-files-9f9a0f8c-9a3c-4a0b-8e6c-6d1f1f7c2b7e

In Tableau, a .hyper file is an extract file created by the Hyper database engine. It contains a snapshot of the data pulled into Tableau and is used to perform data analysis without the need for a live connection to the data source.

To resume a suspended alert, you need to go to the My Content area of Tableau web pages, where you can see all the alerts that you own or subscribe to. You can click on the alert name and then select Resume from the menu. You can also edit or delete the alert from there. References: https://help.tableau.com/current/pro/desktop/en-us/alerts.htm https://help.tableau.com/current/pro/desktop/en-us/alerts_manage.htm

In Tableau, the " My Content " area on the web interface is the place where a user can manage their content, including workbooks, data sources, and alerts. If an alert is suspended, the owner of the alert can go to the " My Content " area to resume it.