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

To ensure that the data is updated every hour, you need to select Extract Refreshes from the data source menu on Tableau Server. This will allow you to add a new extract refresh schedule or edit an existing one. You can specify the frequency and time of the refresh, as well as any custom options or credentials. New will create a new data source, Connected Workbooks will show you which workbooks use this data source, and Connections will show you which tables or files are connected to this data source. References: https://help.tableau.com/current/server/en-us/refresh_extracts.htm https://help.tableau.com/current/server/en-us/refresh_extracts_add.htm

To ensure that the data is updated every hour on Tableau Server, you would need to go to the " Extract Refreshes " option. Here, you can schedule the data extracts to refresh at the desired frequency, which in this case would be every hour.

Extensions in Tableau are web-based programs that can interact with dashboards. To add a button that allows users to export the dashboard as an image, you would use an Extension object. This could be a custom extension designed to trigger the download of the dashboard view as an image.

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.

A suspended data-driven alert can be managed from the My Content area in Tableau’s web interface. Tableau’s current documentation states that when an alert has failed enough times to become suspended, an alert owner can locate the suspended alert in My Content and choose Resume Alert. Tableau now also supports resuming an alert directly from the notification email or from the Alerts panel of the affected view, but neither of those choices appears in this question. Shared with Me manages content shared by other users; Tableau Prep’s notification area does not administer Tableau Server or Cloud data alerts; and Tableau Desktop’s Data Source page is unrelated to server-side alert scheduling. Therefore, among the supplied answers, C remains valid.

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

Tableau CASE logic evaluates one expression and compares its result against the values provided in successive WHEN clauses. In option B, the initial expression evaluates whether aggregated Profit is greater than zero, producing a Boolean result. WHEN TRUE then returns “Profitable,” while the ELSE branch returns “Unprofitable.” The other choices do not follow valid CASE comparison structure. Option A attempts to use OR between string literals inside one WHEN value. Option C places the comparison operators before values rather than supplying values that can be compared against the CASE expression. Option D similarly combines a string literal with an AND condition in a WHEN clause that is being compared against Category. Tableau’s documented CASE structure is CASE < expression > WHEN < value > THEN < result > ... ELSE ... END, making B the valid choice among those provided.

To add two sheets to the dashboard that support the Show/Hide button, you can add them to either a horizontal or a vertical container. A container is an object that can hold one or more sheets or other objects and allow you to arrange them in a layout. A Show/Hide button is a feature that lets you hide or show a container and its contents on the dashboard. You can add a Show/Hide button to any container, whether it is tiled or floating, by using the drop-down menu of the container and selecting “Add Show/Hide Button.” You can also customize the appearance and behavior of the button.

To add a horizontal or a vertical container to the dashboard, you can drag them from the Objects pane to the dashboard. A horizontal container will arrange the sheets or objects horizontally, while a vertical container will arrange them vertically. You can then drag the sheets you want to add from the Sheets pane to the container. You can resize and reorder the sheets or objects within the container by using the handles and arrows.

The other objects in the Answer Area, such as text, image, web page, blank, navigation, download, and extension, do not support the Show/Hide button. They are either static elements that cannot hold other objects, or dynamic elements that require user interaction or external sources. You can add them to the dashboard as well, but they will not have the same functionality as a container with a Show/Hide button.

In Tableau, the RANK function assigns a rank to each row within a partition of the data, based on the value of the field being ranked. It is important to understand that the rank is recalculated whenever the underlying data or the partitioning changes.

In the given scenario, the Overall Rank is based on the Sales figures, while the Rank (presumably) is based on the Sales within the Category. When filtering on the condition where [Overall Rank] - 1, it means we are excluding the sub-category that has an Overall Rank of 2.

Looking at the data:

Furnishings has an Overall Rank of 8, which does not meet the filter condition ([Overall Rank] - 1). Therefore, its rank remains the same.

Tables have an Overall Rank of 3. When the sub-category with an Overall Rank of 2 is removed (Chairs in this case), Tables move up in the overall ranking. However, since Tables are the top-ranked within the Furniture category, their Rank within the category would remain unchanged at 1.

Chairs have an Overall Rank of 2, which meets the filter condition and thus will be removed from the view. We cannot determine the change in Rank for Chairs because they are filtered out.

Accessories have an Overall Rank of 5. If any sub-category with a higher Overall Rank (1 to 4) is removed, the rank of Accessories within the Technology category could change because it is currently ranked 3 in its category. With the removal of Phones (Overall Rank 1), the Rank of Accessories could potentially increase.

Copiers have an Overall Rank of 6, which does not meet the filter condition. Therefore, its rank remains the same.

Machines have an Overall Rank of 4. If we remove Phones (Overall Rank 1), Machines will move up in the overall ranking and potentially within the Technology category as well, changing its Rank from 2 to 1.

Phones have an Overall Rank of 1, which does not meet the filter condition of being Overall Rank 2. Therefore, its rank remains the same.

Bookcases have an Overall Rank of 7, which does not meet the filter condition. Therefore, its rank remains the same.

Based on this analysis, when the sub-category with an Overall Rank of 2 (Chairs) is removed, the Rank value will change for Tables, Accessories, and Machines, as they will move up in the overall ranking within their respective categories. However, it ' s important to note that while Tables will move up in the overall ranking, their rank within the Furniture category would not change as they are already at the top. The rank changes for Accessories and Machines are due to the removal of Phones, which is ranked higher overall and within the Technology category.

To create a boolean calculated field to place on the data source filter, you should use the formula USERNAME() = [Tableau_user]. This formula will return TRUE if the current Tableau user name matches the value in the Tableau_user field, and FALSE otherwise. You can use this formula as a data source filter by dragging it to the Filters shelf and selecting TRUE from the menu. This will filter the data to show only the rows that are relevant to the specific Tableau user.

The other options are not correct for this scenario. NAME([Tableau_user]) is not a valid function in Tableau. ISFULLNAME([Tableau_user] ) = USERNAME() is not a valid expression in Tableau. ‘S’ + STR([Sales]/1000) is not a boolean expression, but a string expression that converts sales to thousands with a prefix of “S”. References: https://help.tableau.com/current/pro/desktop/en-us/filtering_datasource.htm https://help.tableau.com/current/pro/desktop/en-us/functions_functions_logical.htm#USERNAME https://help.tableau.com/current/pro/desktop/en-us/calculations_calculatedfields.htm

In Tableau, the USERNAME() function returns the username of the user who is currently logged in. To create a filter that only shows data relevant to the logged-in Tableau user, a boolean calculated field can be created to compare the current username with the usernames listed in the Tableau_User_Name field of the dataset. Therefore, the correct formula for this filter is USERNAME() = [Tableau_USER_NAME] , which will return true for rows where the Tableau_User_Name matches the current user ' s username.

To ensure that users can see other published dashboards from their current dashboard when publishing a workbook to Tableau Server, you need to enable the option that shows sheets as tabs. This allows users to navigate between different sheets (dashboards) within the published workbook.

In the provided Publish Workbook to Tableau Server dialog box, you should check the option labeled:

" Show sheets as tabs "

This setting will display the sheets as tabs on Tableau Server, enabling users to switch between dashboards easily.

The REPLACE function is used to replace all occurrences of a substring within a string with another substring. In this case, it can be used to remove the semicolons from the Name field by replacing them with an empty string. For example, REPLACE (“John;Doe”, “;”, “”) = “JohnDoe”. References: The information is based on the following sources:

String Functions - Tableau

Remove special characters and add a semi colon in a word - Tableau

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.

Revenue and Quantity are the two quantitative fields that must be measured to calculate average revenue per item. The analyst can derive the required business metric by relating the monetary Revenue measure to the Quantity measure, such as revenue divided by units sold at the appropriate aggregation level. Customer Region is required to segment the analysis geographically, but it is a dimension rather than the numeric value being measured. Order Date provides the time dimension for analysing the metric over time, and Product Name can further categorise the data, but neither is the primary quantitative measure required by the question. Tableau defines measures as quantitative fields that can be numerically aggregated, while dimensions are qualitative fields used to group or segment the data. Therefore, Revenue and Quantity are the appropriate selections.

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.

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.

To create the dashboard as shown in the image, you need to use a vertical layout container that will adjust the height of the sheets according to the window size. You also need to set the Fit to Entire view option for both sheets so that they will resize proportionally when you click a category on the top sheet. This way, you can achieve the desired effect of having the bottom sheet expand to fill the space left by the top sheet when it filters out some categories. References:

Size and Lay Out Your Dashboard - Tableau

Create a Dashboard - Tableau

Format Dashboard Layout in Tableau - GeeksforGeeks

To schedule a refresh of the data, you need to ensure that Tableau Server or Tableau Online can access the database without prompting for credentials. This can be done by embedding the credentials in the workbook or the published data source. You also need to extract the data from the database and publish it as a separate data source or as part of the workbook. A live connection would not allow you to refresh the data on a sched ule, as it would always query the database directly. A hyper file is a format for storing extracted data, but it is not a requirement for scheduling a refresh. References:

Tableau Certified Data Analyst Exam Prep Guide , page 10, section “Publishing and Scheduling Data Refreshes”

Tableau Help: Embedding Database Credentials

Tableau Help: Extract Your Data

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

Tableau ' s Layer Control is specifically designed for maps containing multiple geographic mark layers. When enabled, it gives users an interactive panel from which individual map layers can be shown or hidden and interactions with particular layers can also be enabled or disabled. Tableau Desktop authors enable this feature through Map > Map Options > Layer Control before publishing the workbook. A parameter could theoretically drive custom calculations, but it is unnecessary because Tableau already supplies native layer-visibility controls. The Filter shelf filters underlying data values rather than directly controlling geographic mark-layer visibility. A dual-axis map is also unnecessary and is more limited than Tableau ' s current multi-layer mapping architecture. Therefore, C is the direct, officially supported method for allowing users to toggle map layers independently.

To add a column that shows the day of the week for each row, you should complete the formula as follows:

DATENAME

(

‘weekday’

, [Order Date])

 The DATENAME function in Tableau returns the name of a specified part of a date or datetime value, such as the year, month, or day. It takes two arguments: the date part and the date. In this question, you want to return the name of the weekday, such as Monday, Tuesday, or Wednesday, for each order date. Therefore, you need to use ‘weekday’ as the date part and [Order Date] as the date. The formula will return a string value that represents the name of the weekday for each order date.

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.

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 correct order of the three actions is:

Add the date to the Columns shelf. Add the segment and the sales to the Rows shelf.

Aggregate the date to month and year.

From the Analytics pane, drag Forecast to the worksheet.

The first action is to add the date to the Columns shelf and the segment and the sales to the Rows shelf. This will create a line chart that shows the daily sales by segment over time. You can use the Show Me menu to choose a line chart if it is not selected by default.

The second action is to aggregate the date to month and year. This will group the daily sales into monthly sales and show the yearly trend. You can right-click on the date field on the Columns shelf and select Month (January 2017) from the menu. You can also drag Year from the Dimensions pane to the Columns shelf before or after Month.

The third action is to drag Forecast from the Analytics pane to the worksheet. This will add a forecast that predicts future sales by segment based on historical trends. You can customize the forecast by clicking on it and using the options on the Marks card.

The other options are not relevant for this scenario. Adding a trend line would show a linear or nonlinear relationship between two measures, but not a prediction of future values. Creating a calculated field that uses the model quantile function would return a value from a statistical model based on a given quantile, but not a forecast.

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 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.

The current view contains only one aggregated Discount mark for each Vendor, so Tableau does not have enough individual marks within each vendor partition to calculate a meaningful distribution. Adding Invoice Number to Detail increases the level of detail so that each invoice contributes an individual Discount mark while Vendor remains the grouping dimension. Tableau can then calculate quartiles, median, whiskers, and outliers from the underlying distribution. Tableau’s box-plot guidance lists a dimension on Detail as one of the fundamental building blocks of a box-and-whisker plot. Changing the whisker configuration would affect how an already valid distribution is displayed but would not create the missing observations. Count Distinct and converting Discount to discrete would alter the analytical meaning rather than establish the correct mark granularity. Therefore, B is correct.

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

A Running Total accumulates the measure sequentially across the addressing dimension. With Year on Columns and SUM(Downloads) in the view, Tableau starts with the first year ' s downloads and progressively adds each subsequent year ' s value to the accumulated total. The resulting bars therefore represent all downloads received up to each year since release rather than the downloads for that individual year alone. YTD Total is intended for accumulation within a calendar year and commonly restarts with the new year. Percent of Total expresses each value as a proportion of the full partition, while Year Over Year Growth compares the relative change between consecutive annual values. Tableau lists Running Total as a standard quick table calculation specifically for cumulative analysis. Therefore, B is the appropriate calculation.

To create a map visualization that shows the concentration of data points in different locations, you need to change the mark type to Density. This will create a heatmap that uses color and size to indicate the density of the data points. You can also adjust the intensity and transparency of the density marks to suit your needs. References: The information is based on the following sources:

Create Heatmaps that Show Trends or Density in Tableau

Build a Simple Map - Tableau

In Tableau dashboards, floating elements are those that can be freely moved over the dashboard canvas without being snapped into the tiled layout. The floating elements are often indicated by their standalone frames and the ability to overlap with other dashboard items. From the provided image, the state filter and the Rate of Obesity chart can be identified as floating if they can be moved independently of the tiled grid layout that constrains other elements.

we need to create a quick table calculation that shows the percent of total sales for each category and sub-category. To do this, we can follow these steps:

Drag Category to Columns and Sub-Category to Rows.

Drag Sales to Text on the Marks card.

Right-click Sales on the Marks card and select Quick Table Calculation > Percent of Total.

Right-click Sales on the Marks card again and select Compute Using > Category.

This will create a quick table calculation that shows the percent of total sales for each sub-category within each category. The visualization will look like this:

To determine the Rank Score value for Student ID 4220, we look at the visualization provided and apply the table calculation of RANK(AVG([Score])) . This function will rank the average scores in ascending order by default, with the lowest score receiving the rank of 1.

Based on the provided visualization, the scores for the students are as follows (from lowest to highest average score):

Student ID 4183: 39 (Rank 1)

Student ID 4220: 71 (Rank 6)

Student ID 4247: 71 (Rank 6, same score as ID 4220 so they share the same rank)

Student ID 4124: 75.5

Student ID 4050: 64

Student ID 4205: 67

Student ID 4056: 66

Since Student ID 4220 has an average score of 71, which is the third-highest score, it shares the rank with Student ID 4247. However, since Tableau ranks without gaps, the actual rank assigned is 6, considering that there are two students with a rank of 4 (due to the same average score being the second lowest), making the next rank number 6.

Note: The actual ranks for the highest scores are not provided, but they are not needed to determine the rank for Student ID 4220.

https://help.tableau.com/current/prep/en-us/prep_calculations.htm

The correct order of the three actions is:

From the connections pane, connect to both data sources

Join both datasets and select the join type

Specify Employee Name as the join key

The first action is to connect to both data sources from the connections pane in Tableau Prep. The connections pane is where you can access and add data sources to your flow. You can connect to various types of data sources, such as Excel, SQL Server, or Tableau Server. In this case, you want to connect to an Excel worksheet and a SQL Server table.

The second action is to join both datasets and select the join type. A join is a way of combining data from two or more tables based on a common field. You can join datasets by dragging one table to the canvas and dropping it on top of another table. This will create a join step in your flow. You can select the join type from the drop-down list on the join step. The join type determines which rows are returned from the tables.

The third action is to specify Employee Name as the join key. A join key is a field that is used to match rows from different tables. You can specify the join key by clicking on the field name in each table and dragging it to the center of the join step. This will create a join clause that shows the field name and the operator. In this case, you want to use Employee Name as the join key, because it is a common field between the two datasets.

The other options are not relevant for this scenario. Specifying Department and Team Size as a join condition will not work, because they are not common fields between the two datasets. Opening both data sources in Tableau Desktop will not help you join them in Tableau Prep. Adding a step to aggregate the data in the SQL Server table will not affect the join, but it may change the level of detail of your data. References: https://help.tableau.com/current/prep/en-us/prep_connect.htm https://help.tableau. com/current/prep/en-us/prep_join.htm https://help.tableau.com/current/prep/en-us/prep_join_types.htm

A reusable custom color palette is defined through Tableau’s preferences configuration rather than by formatting a single worksheet. The Preferences.tps file resides in My Tableau Repository and uses XML tags to define named color palettes and their color values. After the palette is added and Tableau Desktop is restarted, the palette becomes available from the color-palette selector and can be used across workbooks, which matches the branding requirement. Custom shapes are unrelated because they control mark appearance rather than color definitions. Worksheet shading affects only formatting in a particular workbook or sheet, and the standard color picker does not create the same reusable named palette in the manner described by this question. Therefore, modifying the XML in Preferences.tps is the correct exam-aligned method.

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.

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.