TDS-C01 Tableau Desktop Specialist Questions and Answers

1) Right click on Rows or Columns, and choose format. In the Font option click on Bold. (CORRECT) - this will modify only the selected axis (row or column)

2) Choose Format then Font from the Menu bar, and select Bold under the Header option (INCORRECT) - this modifies both rows and columns at the same time

3) Choose Format from the Menu bar, select Row or Column, and then select Bold under the header option (CORRECT) -  this will modify only the selected axis (row or column)

4) Select Text Label on the Marks Card, choose Rows or Columns, and then select Bold. (INCORRECT) - no such option exists

In Tableau, you can change the font of a worksheet title by double-clicking directly on the title in the view, which opens a dialog box where you can format the text, including changing the font. Another method is to use the Format menu; from there, you select "Title and Caption," which opens the Format Title pane on the left side of the screen, where you can change the font and other formatting options for the worksheet title.

Explanation

If you chose 2, then you were very close but probably didn't plot the actual Percent Difference on your view. ( One of the marks is just over the line).

Firstly, Drop the Week ID onto the column shelf, and convert it to continuous since we need both Year and Quarter as mentioned in the question.

2) Next, Drop assortment to filters shelf so that we can focus on Electronics!

3) This should be your view now. Click on the Show Mark Labels icon as shown:

The final view is as follows, with 3 points below 0 ( i.e negative )

In Tableau, you can rename a field directly in the Data pane by clicking and holding on the field name until it becomes editable, allowing you to type a new name. Alternatively, you can use the drop-down menu associated with the field in the Data pane and select the "Rename" option. Both methods provide a quick and easy way to change the name of a field without affecting the underlying data structure. The "Format menu" and "Replace References" option do not apply to renaming fields.

Explanation

You can create aliases (alternate names) for members in a dimension so that their labels appear differently in the view.

Aliases can be created for the members of discrete dimensions only. They cannot be created for continuous dimensions, dates, or measures.

To create an alias:

1) In the Data pane, right-click a dimension and select Aliases.

2) In the Edit Aliases dialog box, under Value (Alias), select a member and enter a new name.

To submit your changes: In Tableau Desktop, click OK.

On Tableau Server or Tableau Online, click the X icon in the top-right corner of the dialog box.

When you add the field to the view, the alias names appear as labels in the view. For example:

We know that continuous fields will always create an axis, so options stating 'header' are automatically eliminated. For our question, see below:

Graphical user interface, text, application, email, Teams Description automatically generated

Had the question asked us to place this pill on the Rows shelf instead, we would've gotten a different answer:

Graphical user interface Description automatically generated with low confidence

Extracts would be more beneficial for analyzing historical prices where we won't be making use of any real time data being streamed. Same is the case for enormous datasets having billions of rows (extracts will be more efficient in analyzing subsets of such large data).

As for live stock prices, flight updates, real time updates from production or mission critical systems - having a live connection is the most logical choice, since we need access to the most fresh and recent data possible at all times!

The Shape option is available for scatter plots and packed bubbles views. The Shape option allows you to change the shape of marks in the view by selecting from a predefined set of shapes or adding custom shapes. You can access the Shape option by placing any field on Shape on the Marks card4 Scatter plots are views that show the relationship between two numerical variables by plotting them as coordinates on a Cartesian plane. You can create a scatter plot by placing at least one measure on Columns and at least one measure on Rows on the Marks card. You can then use Shape to assign different shapes to different categories or segments in your data5 Packed bubbles are views that show hierarchical data as a set of nested circles. Each circle represents a dimension member and its size is proportional to a measure value. You can create a packed bubble chart by placing one or more dimensions on Detail and one measure on Size on the Marks card. You can then use Shape to change the shape of circles to other shapes such as squares or stars6 The other options are not valid views for using the Shape option. Side-by-side circles are views that show proportions of a whole by using circles with different angles and sizes arranged horizontally or vertically. You can create a side-by-side circle chart by placing one dimension on Columns or Rows and one measure on Angle and Size on the Marks card. You cannot use Shape to change the shape of circles in this view7 Heat maps are views that show the distribution of two or more measures by using a color gradient and size. You can create a heat map by placing one or more dimensions on Columns and Rows and two measures on Color and Size on the Marks card. You cannot use Shape to change the shape of marks in this view8

Explanation

Not too tough. Follow along the steps:

Drag Color to Filter and choose Red:

3) Now to display the percentage correctly, lets format it. Click on the In Stock % pill in the Row shelf, and select format:

And your final view will look like :

Explanation

Tableau displays data source connections and data fields for the workbook in the Data pane on the left side of the workspace.

The Data pane includes:

Dimension fields – Fields that contain qualitative values (such as names, dates, or geographical data). You can use dimensions to categorize, segment, and reveal the details in your data. Dimensions affect the level of detail in the view. Examples of dimensions include dates, customer names, and customer segments.

Measure fields – Fields that contain numeric, quantitative values can be measured. You can apply calculations to them and aggregate them. When you drag a measure into the view, Tableau applies an aggregation to that measure (by default). Examples of measures: sales, profit, number of employees, temperature, frequency.

For more information on what dimensions and measures are, see Dimensions and Measures, Blue and Green.

Calculated fields – If your underlying data doesn't include all of the fields you need to answer your questions, you can create new fields in Tableau using calculations and then save them as part of your data source. These fields are called calculated fields.

For more information on calculated fields, see Create Custom Fields with Calculations.

Sets – Subsets of data that you define. Sets are custom fields based on existing dimensions and criteria that you specify. For more information, see Create Sets.

Named sets from an MS Analysis Services server or from a Teradata OLAP connector also appear in Tableau in this area of the Data pane. You can interact with these named sets in the same way you interact with other custom sets in Tableau.

Parameters – Values that can be used as placeholders in formulas, or replace constant values in calculated fields and filters. For more information, see Create Parameters.

Of course! As an example, see below:

1) Using the Sample Superstore data, let's plot a bar chart showing the Sales for each Category:

Observe that we have 3 marks - Each bar in a bar chart is called a mark. Similarly, each point in a scatter plot is also a mark , and so on for all charts.

1 row by 3 columns means that clearly on the y-axis (Sales), we have only a single mark - a single continous axis, but 3 different marks (Technology, Furniture and Office supplies ) on the x-axis.

2) Now let's add subcategory to the view as well (another dimension):

Observe that the number of marks has increased - i.e the number of Bars.

Also, notice we now have 1 row and 17 columns. Simply because 1 row = Sales (on the y-axis), and on the x-axis, we have 17 different columns (i.e product sub categories!!)

Explanation

If you chose 111,060 you were SO close to the correct answer but made a small mistake - you didn't change the aggregation to AVERAGE! This is one of the common mistakes many test takers make, so keep this in mind.

To reach the correct answer, follow the steps below:

1) Draw Assortment to the Column shelf, and drag Year to the Rows Shelf. Then Drill down further on Year to accomodate Quarters and Months as well!

Although this seems enough, DON'T FORGET to change the aggregation like in the next step, which will completely change the values!

The correct answer as you can see is 1,461 - Sales for Harware Assortment in 2017 Q4, October

Explanation

By default, all filters that you set in Tableau are computed independently. That is, each filter accesses all rows in your data source without regard to other filters. However, you can set one or more categorical filters as context filters for the view. You can think of a context filter as being an independent filter. Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter.

You may create a context filter to:

1) Improve performance – If you set a lot of filters or have a large data source, the queries can be slow. You can set one or more context filters to improve performance.

2) Create a dependent numerical or top N filter – You can set a context filter to include only the data of interest, and then set a numerical or a top N filter.

To assign a color to marks in the view, do the following:

From the Data pane, drag a field to Color on the Marks card.

Tableau applies different colors to marks based on the field’s values and members. For example, if you drop a discrete field (a blue field), such as Category, on Color, the marks in the view are broken out by category, and each category is assigned a color.

If you drop a continuous field, such as SUM(sales), on Color, each mark in the view is colored based on its sales value.

Explanation

Extracts are advantageous for several reasons:

1) Supports large data sets: You can create extracts that contain billions of rows of data.

2) Fast to create: If you're working with large data sets, creating and working with extracts can be faster than working with the original data.

3) Help improve performance: When you interact with views that use extract data sources, you generally experience better performance than when interacting with views based on connections to the original data.

4) Support additional functionality: Extracts allow you to take advantage of Tableau functionality that's not available or supported by the original data, such as the ability to compute Count Distinct.

5) Provide offline access to your data: Extracts allow you to save and work with the data locally when the original data is not available. For example, when you are traveling.

To work with the MOST up-do-date data, use a live connection instead!

When using a data source that has relationships, you can add a join between two or more tables by dragging a table directly on top of an existing logical table from the Tables pane. This will create a logical table that contains the joined tables. You can then edit the join type, join clauses, and join calculation as needed2 The other options are not valid ways to add a join between tables when using relationships. From the Tables pane, double-clicking a table will add it to the data source as a separate logical table, not joined with any other table. From the Connections pane, selecting Add will allow you to add another connection to the data source, not join tables within the same connection. Double-clicking a logical table in the canvas will open the Data Source page, where you can view and edit the fields in the logical table, not join it with another table2

When the Physical Tables option is selected for an extract in Tableau, an individual table is created for each physical table in the extract. This means that the extract will include a separate table for each underlying table in your database, maintaining the database's structure within the extract. This can be useful when you need to preserve the original granularity of the data or when working with certain database optimizations.

If you’ve created a data connection that you might want to use with other workbooks or share with colleagues, you can export (save) the data source to a file. You might want to do this also if you’ve added joined tables, default properties, or custom fields—such as groups, sets, calculated fields, and binned fields—to the Data pane.

Visualisations and Data extracts are NOT saved in a .tds file!

View Data allows you to inspect your data in a spreadsheet-like layout. You can view data either for the data source as a whole, or to see the underlying data for an individual mark or a group of marks. In a worksheet, the rows that you see in the View Data window are always scoped to the current selection or the current view.

The View Data window displays as much of the data as possible by default, up to 10,000 rows. Field names are shown as column headers and can be dragged and dropped to change their display order. Click a column header to sort the values in that column.

From the official website:

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To clean and automatically fix the data issues in our data source - This is the definition of Data Interpreter.

To rename the field in the original data source - We never modify the original data source when managing metadata. All changes are local to Tableau for our convenience only.

All other options can be modified using the Manage Metadata property.

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You can assign an alias to a field member before creating a visualization. An alias is an alternative name that you can assign to a value in a dimension field. You can use aliases to rename specific values within a dimension to make them more relevant or descriptive in your view than what the original data provides. For example, you can use aliases to shorten long names, correct spelling errors, or replace codes with meaningful labels6 You can create aliases for the members of discrete dimensions only. They cannot be created for continuous dimensions, dates, or measures. To create an alias for a dimension member, you can right-click the dimension in the Data pane and select Aliases, then enter a new name for each member under Value (Alias). You can also create an alias by right-clicking a dimension member in the view and selecting Edit Alias. You can do this before or after creating a visualization6 The other options are not accurate statements about aliases. You cannot create an alias for a discrete measure, because measures are not discrete fields. You cannot create an alias for a continuous dimension, because aliases are only available for discrete dimensions. When you assign an alias, the name does not change in the database, only in Tableau. Aliases are stored as part of the workbook or data source, and do not affect the original data6

Explanation

Line charts connect individual data points in a view. They provide a simple way to visualize a sequence of values and are useful when you want to see trends over time, or to forecast future values.

Please refer to the images below:

According to the official Tableau documentation:

Explanation

When using a Percent difference, Tableau calculates what the percent change has occured as compared to the last data value. BUT, for the first data value, there is no previous value to compare it to. Hence, it appears as NULL.

According to the Tableau Desktop Specialist Exam Readiness, to create a calculation that returns a customer name followed by a comma, a space, and then the customer’s age, you should use the formula [Customer Name] + “,” + STR([Age]). This is because you need to concatenate strings using the + operator, and convert the numeric field [Age] to a string using the STR() function.

Explanation

According to the official Tableau documentation, there are 5 types of trend lines which we can work with in Tableau :

1) Linear Trend Line

2) Logarithmic Trend Line

3) Exponential Trend Line

4) Polynomial Trend Line

5) Power Model

Hence, the correct answer is BINOMIAL trend line which is not present in Tableau.

See the following image:

For more information, refer to: https://help.tableau.com/current/pro/desktop/en-us/trendlines_add.htm

Explanation

THIS IS A VERY IMPORTANT QUESTION

To answer this question, you need to understand Tableau's Order of Operations. See below and remember this always:

A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures. Below is a single bullet graph showing how actual sales compared to estimated sales.

We can create a Bullet graph with just 2 measures! This method requires the LEAST number of fields possible to create this type of chart.

The best way to tackle such questions in the exam is to click the "SHOW ME" button on top right, and hover over the chart we want to create.

In our case, it is a Bullet graph.

Therefore, we need 2 measures at least to create this chart, and 0 or more dimensions.

Explanation

Data fields are made from the columns in your data source. Each field is automatically assigned a data type (such as integer, string, date), and a role: Discrete Dimension or Continuous Measure (more common), or Continuous Dimension or Discrete Measure (less common).

Dimensions contain qualitative values (such as names, dates, or geographical data). You can use dimensions to categorize, segment, and reveal the details in your data. Dimensions affect the level of detail in the view.

Measures contain numeric, quantitative values that you can measure. Measures can be aggregated. When you drag a measure into the view, Tableau applies an aggregation to that measure (by default).

Yes, due to the nature of blends, there are some conditions as follows from the official documentation that must be kept in mind while working with blends:

In addition to handling calculations slightly differently, there are some limitations on secondary data sources. You may not be able to sort by a field from a secondary data source, and action filters may not work as expected with blended data. For more information, see Other data blending issues.

Explanation

You can create a group to combine related members in a field. The icon associated with a group is a paper clip!

You should use a relationship instead of a join for two data sets when the data sets include similar data aggregated at different levels of detail. A relationship is a way of combining data from different tables based on common fields without creating a single table with all fields. A relationship allows you to query data from multiple tables as needed and preserve the level of detail of each table. A relationship is useful when you have data sets that are aggregated at different levels of detail, such as sales by region and sales by product. A relationship can match data from different levels of detail without creating null values or duplicate rows. The other options are not valid situations for using a relationship instead of a join for two data sets. The data sets include similar data aggregated up to the highest level is not correct, because a relationship is not needed when both data sets have the same level of detail. You can use either a relationship or a join in this case, depending on your preference and performance. The data sets are in two separate tables within a single spreadsheet is not correct, because a relationship is not limited by the location or format of the data sets. You can use a relationship to combine data from different sources or connections, such as databases, files, or web services. To use both data sets across multiple sheets in a visualization is not correct, because a relationship does not affect how you use data in your visualization. You can use either a relationship or a join to create multiple sheets and dashboards with your data sets.

Explanation

Whenever you add a measure to your view, an aggregation is applied to that measure by default. This default is controlled by the Aggregate Measures setting in the Analysis menu.

If you decide you want to see all of the marks in the view at the most detailed level of granularity, you can disaggregate the view. Disaggregating your data means that Tableau will display a separate mark for every data value in every row of your data source.

To disaggregate all measures in the view:

Clear the Analysis >Aggregate Measures option. If it is already selected, click Aggregate Measures once to deselect it.

THIS IS A VERY IMPORTANT QUESTION

Aggregation is an important concept to consider when creating calculated fields. A calculated field for SUM([Profit]) / SUM([Sales]) will give you a very different answer than [Profit] / [Sales], even though both formulas are valid.

If you do not provide the aggregation within the calculated field, Tableau will calculate the equation for every record ( row ) in your analysis, then aggregate the answers for all of the rows together when the calculated field is added to the view.

In simple terms, if specify the aggregation such as SUM, what Tableau will do is that it will first calculate the sum of the Profit column ( say x ), then calculate the sum of the Sales column ( say y ), and then simply apply x/y ---> This is what we expect! Perfect!

BUT, if you don't specify the aggregation, it will go to every single ROW, perform Profit / Sales, and then aggregate the answers calculated for each row. This is simply NOT what we wan't!

An example:

It contains all the measures in your data, collected into a single field with continuous values - This is the definition for 'Measure Values'.

All others are True w.r.t. Measure Names!

The Measure Names field contains the names of all measures in your data, collected into a single field with discrete values.

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

Explanation

The data that you analyze in Tableau is often made up of a collection of tables that are related by specific fields (that is, columns). Joining is a method for combining data on based on those common fields. The result of combining data using a join is a virtual table that is typically extended horizontally by adding columns of data.

For example, consider the following two tables originating from a single data source:

We can combine these 2 tables, simply by joining the tables on ID to answer questions like, "How much was paid in royalties for authors from a given publisher?". By combining tables using a join, you can view and use related data from different tables in your analysis.

By default, all filters that you set in Tableau are computed independently. That is, each filter accesses all rows in your data source without regard to other filters. However, you can set one or more categorical filters as context filters for the view. You can think of a context filter as being an independent filter (Option stating - To create a dependent filter eliminated here). Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter.

You may create a context filter to:

1) Improve performance – If you set a lot of filters or have a large data source, the queries can be slow. You can set one or more context filters to improve performance.

2) Create a dependent numerical or top N filter – You can set a context filter to include only the data of interest, and then set a numerical or a top N filter.

For example, suppose you’re in charge of breakfast products for a large grocery chain. Your task is to find the top 10 breakfast products by profitability for all stores. If the data source is very large, you can set a context filter to include only breakfast products. Then you can create a top 10 filter by profit as a dependent filter, which would process only the data that passes through the context filter.

You can perform two actions when you join tables from multiple connections: add a data source filter and create an extract. A data source filter is a filter that you can apply to the data source before it is loaded into Tableau. This can help improve performance and reduce the amount of data in the view. A data source filter can be applied to any data source, including those that use cross-database joins2 An extract is a snapshot of data that is stored locally on your computer or on Tableau Server. An extract can also improve performance and enable offline analysis. You can create an extract from any data source, including those that use cross-database joins3 You cannot perform the other two actions when you join tables from multiple connections: create a union or use a stored procedure. A union is a method for combining data by appending rows of one table onto another table. To union your data in Tableau, the tables must come from the same connection. You cannot union tables from different data sources or connections4 A stored procedure is a set of SQL statements that can be executed on a database server. Tableau does not support using stored procedures as part of a cross-database join. You can only use stored procedures with some single-connection relational data sources, such as Microsoft SQL Server, Oracle, or PostgreSQL5

You can use Wildcard Search to set up search criteria to automatically include tables in your union. Use the wildcard character, which is an asterisk (*), to match a sequence or pattern of characters in the Excel workbook and worksheet names, Google Sheets workbook and worksheet names, text file names, JSON file names, .pdf file names, and database table names.

When working with Excel, text file data, JSON file, .pdf file data, you can also use this method to union files across folders, and worksheets across workbooks. Search is scoped to the selected connection. The connection and the tables available in a connection are shown on the left pane of the Data source page.

Explanation

We can drag fields from the Data pane to create the structure for your visualizations.

The Columns shelf creates the columns of a table, while the Rows shelf creates the rows of a table. You can place any number of fields on these shelves.

These FIELDS are also referred to as PILLS. See below:

Explanation

The INNER JOIN keyword selects all rows from both tables as long as there is a match between the columns. Consider 2 tables "Orders" and "Customers".

If there are records in the "Orders" table that do not have matches in "Customers", these orders will not be shown!

The LATEST Tableau Desktop Sepcialist exam blueprint now requires you to know some basics about animations as well!

NOTE: Animations are DISABLED by default and must be manually enabled.

You can also reset all settings to default by clickin on 'Reset All'

To combine data from another database with an existing data source in Tableau, the first step is to add a new connection. This will allow you to access the additional database and either blend or join the data from the two sources depending on your analysis needs.

According to the [Tableau Desktop Specialist Exam Guide], an incremental refresh only adds rows that are new, based on a specified column and value. A full refresh replaces all the extracted data with the data in the underlying data source. A full refresh is usually very slow, especially for large extracts. An incremental refresh can take less time, depending on how many new rows are added. A full refresh does not need to be configured, it is the default option for extracts in Tableau. An incremental refresh can be run from both Tableau Desktop and Tableau Server.

Hierarchies in Tableau are used to define a drill-down path through your data. By creating a hierarchy, you can organize related fields together, which makes it easier to navigate complex data models. This also allows users to explore data at different levels of detail, from the highest level of the hierarchy to the most granular details, simply by clicking to expand and collapse levels of the hierarchy in the view.

When creating a histogram in Tableau, bin size refers to the range of the continuous measure counted in each bin. A histogram is a chart that displays the shape of a distribution of a continuous measure. A histogram looks like a bar chart but groups values for a continuous measure into ranges, or bins. The basic building blocks for a histogram are as follows: Mark type: Automatic; Rows shelf: Continuous measure (aggregated by Count or Count Distinct); Columns shelf: Bin (continuous or discrete)4 To create bins from a continuous measure, you need to specify the size of bins, which determines how many bins are created and how wide they are. The size of bins is equal to the difference between consecutive values along the axis that represents the bins. For example, if you have bins with values 0-10, 10-20, 20-30, etc., then the size of bins is 10. You can either enter a value for the size of bins manually or have Tableau suggest an optimal bin size based on a formula that considers the number of distinct rows and the minimum and maximum values in the data5 The other options are not valid definitions of bin size when creating a histogram in Tableau. The minimum number of axis ticks in the view is determined by Tableau’s automatic scaling and formatting of axes, which can be adjusted manually if needed. The count distinct (COUNTD) of items on either axis is an aggregation function that returns the number of unique values in a field, which can be used as a measure in a histogram but not as bin size. The maximum number of marks in the view is limited by the performance and readability of the visualization, which can be improved by filtering, sorting, or aggregating the data4

In the context of Tableau, "Migrated Data" typically refers to data or workbooks that have been upgraded from a previous version of Tableau Desktop. When you open a workbook in a newer version of Tableau, the data sources within that workbook might be labeled as "Migrated Data," indicating that they have undergone a conversion process to be compatible with the new version's features and architecture.

Explanation

TWBX is all in one. It contains viz, info needed to build the viz, and a copy of the data source. It doesn’t contain extracts of the data but can contain both live and data extracts. Best if want to eliminate the barrier of data access.

Create a .twbx with file-based data sources

1) Select File > Save As.

2) Specify a file name for the packaged workbook in the Save As dialog box.

3)Select Tableau Packaged Workbooks on the Save as type drop-down list.

4) Click Save.

5) The default location is the Workbooks folder of the Tableau repository. However, you can save packaged workbooks to any directory you choose.

The following files are included in packaged workbooks:

--> Background images

--> Custom geocoding

--> Custom shapes

--> Local cube files

--> Microsoft Access files

--> Microsoft Excel files

--> Tableau extract files (.hyper or .tde)

--> Text files (.csv, .txt, etc.)

Date values in Tableau represent specific points in time and are typically formatted in a standard date format.

Option A, "2020-05-01", is a standard date format representing the 1st of May, 2020.

Option D, "January 1, 1995", is another example of a date value, representing the 1st of January, 1995. Options B ("December") and C ("Wednesday") represent a month and a day of the week, respectively, but do not specify a particular date.

Explanation

Measures contain numeric quantitative values hence that option is incorrect.

Reference 1: https://help.tableau.com/current/pro/desktop/en-us/datafields_typesandroles.htm

Reference 2: https://help.tableau.com/current/pro/desktop/en-us/dates.htm

Names and Categories would be mostly used as dimensions (categorical data).

Profit and measures contain quantitative data and would be more suitable for Measures!

Only 1 option is correct -

Click on Dashboard in the Menu bar followed by Copy Image

Right click on the dashboard, and choose Copy, then image - Try doing this, you will end up copying just one of the worksheets not the entire dashboard

Click on Worksheet in the Menu bar followed by Export, then choose Image - Again, try doing this. You will end up copying just one of the worksheets not the entire dashboard

Using the floating export worksheet option on the Dashboard - No such option exists

Hierarchies in Tableau allow users to drill up and down to explore data at different levels of detail. By setting up a hierarchy, users can navigate through levels of the hierarchy to analyze data at each level, from the highest summary down to the finest detail.

When creating a date filter in Tableau, anchor refers to the date in a relative-date filter to which you anchor a unit of time. A relative-date filter is a type of filter that allows you to dynamically filter data based on a range of time relative to the current date or a specific date. For example, you can use a relative-date filter to show data for the last 7 days, the next 3 months, or the current quarter1 To create a relative-date filter, you need to specify the following options: The date field that you want to filter. The unit of time that you want to use for the filter, such as days, weeks, months, quarters, or years. The range of time that you want to show relative to the anchor date, such as last, next, this, or to date. The anchor date that you want to use as the reference point for the filter. You can use the current date or a custom date as the anchor date1 For example, if you want to create a relative-date filter that shows data for the last 3 months from January 1st, 2022, you would select the following options: Date field: Order Date Unit of time: Months Range of time: Last 3 Anchor date: Custom (01/01/2022) 1 The other options are not valid definitions of anchor when creating a date filter in Tableau. The mid-point in the range of displayed dates is not related to the anchor date, but rather to the center of the axis that represents the date field. The date in a range-of-dates filter to which you anchor units of time is not correct, because a range-of-dates filter does not use an anchor date, but rather a start and end date that can be fixed or dynamic. The latest date in the data source that can be found is not related to the anchor date, but rather to the maximum value of the date field in the data source2

Explanation

A geographic role associates each value in a field with a latitude and longitude value.

Assigning a geographic role based on the type of location (such as state versus postcode) helps ensure that your data is plotted correctly on your map view. For example, you can assign the City geographic role to a field that contains a list of city names.

To assign a geographic role to a field:

In the Data pane, click the data type icon next to the field, select Geographic Role, and then select the geographic role you want to assign to the field.

When you assign a geographic role to a field, Tableau adds two fields to the Measures area of the Data pane: Latitude (generated) and Longitude (generated).

These fields contain latitude and longitude values and are assigned the Latitude and Longitude geographic roles. If you double-click each of these fields, Tableau adds them to the Columns and Rows shelves and creates a map view using the Tableau background map.

The icon shown is used to add a new Dashboard! From the official documentation:

Explanation

As the names suggest, if we subtract the order date from the shipping date, we simply get the number of days between these 2 dates.

We can these use this calculated field in our charts, and can use COUNT, SUM, AVG etc with them according to our need.

Explanation

If you answered this question quickly and correctly, you're well prepared for the exam! Most students stumble while creating a Dual axis chart, so go ahead and give yourself a pat on the back!

To create a dual axis chart for the problem mentioned:

1) Drag Product Type to the column shelf, and Sales and Profit to the Row shelf:

2) Now, to focus on 2013, drag Date to the filter shelf and select only 2013:

3) Now, click on the Profit pill in the Rows Shelf, and select dual axis:

4) Now, in the marks shelf, choose Sales, and change the chart type to bar. Similarly, for Profit, change the chart type to Line.

Now the chart looks like this:

Now we change the Profit chart type to line:

5) Finally, we synchronise the axis as follows: Right click on the axis, and choose 'Synchronise axis'

And, our final view and answer is:

From the official documentation:

The default view that you first see in the Data Source page canvas is the logical layer of the data source. You combine data in the logical layer using relationships (or noodles).

Layout containers let you group related dashboard items together so you can quickly position them. As you change the size and placement of items inside a container, other container items automatically adjust

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The correct answer is Toggle the highlighting ON/OFF. If selected, whichever value you choose from this legend will be highlighted in the view. However, if it is deselected, then even if you choose a value in the Legend, it will NOT be highlighted.

See below:

Chart, bar chart Description automatically generated

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Explanation

Tableau Public is a free service that lets anyone publish interactive data visualizations to the web. Visualizations that have been published to Tableau Public (“vizzes”) can be embedded into web pages and blogs, they can be shared via social media or email, and they can be made available for download to other users.

Check it out : https://public.tableau.com/en-us/s/

Explanation

A histogram is a chart that displays the shape of a distribution. A histogram looks like a bar chart but groups values for a continuous measure into ranges, or bins.

Using the Sample superstore as a reference:

1) Let's plot a bar chart showing SUM(Quantity) for each Segment:

2) Right click on the x-axis (Segment):

Notice we don't have an option to edit the axis, only header. This is because only continous values form the AXIS.

3) Similarly, right click on the y-axis (Quantity):

Now we have the option to edit BOTH the axis and the header.

*Very important question*

If we select the labels in the view and then group, a new consolidated mark is created - in our case bar since we are talking about a bar chart in the question. See below:

Then on grouping, a new bar is created, and the colour of all bars remain the same.

Had we grouped by choosing the marks instead of the labels, the following would be the result:

Explanation

There are two types of sets: dynamic sets and fixed sets. The members of a dynamic set change when the underlying data changes. Dynamic sets can only be based on a single dimension.

To create a dynamic set:

1) In the Data pane, right-click a dimension and select Create > Set.

2) In the Create Set dialog box, configure your set. You can configure your set using the following tabs:

General: Use the General tab to select one or more values that will be considered when computing the set.

You can alternatively select the Use all option to always consider all members even when new members are added or removed.

None of the other options exist, and therefore are incorrect answers.

Explanation

According to Tableau's official documentation -

After creating a dual axis chart, make sure to synchronise their axis since they both might not be having the same y-axis.

To align the two axes in a dual axes chart to use the same scale, right-click (control-click on Mac) the secondary axis, and select Synchronize Axis. This aligns the scale of the secondary axis to the scale of the primary axis.

In this example, the Sales axis is the secondary axis and the Profit axis is the primary axis.

If you would like to change which axis is the primary, and which axis is the secondary, select the field on the Columns or Rows shelf that is the secondary, and drag it in front of the primary field on the shelf until you see an orange triangle appear.

In this example, you can select the SUM(Sales) field on the Rows shelf, and drag it in front of the SUM(Profit) field. The Sales axis is now the primary and the Profit axis is the secondary.

Measure Names is a dimension. Latitude, Longitude, and Number of records are all measures.

Explanation

Relative date filters dynamically update to show a time period relative to when you open the view, such as the current week, the year to date, or the past 10 days. Relative date filters make it easy to create views that always show the most recent data.

Step 1: Drag a date field to the filter shelf

Right-click (control-click on Mac) and drag a date field from the Data window to the Filters shelf. In the Filter Field dialog box, click Relative Date, and then click Next.

Step 2: Select a time unit

In the Filter dialog box, click Relative dates, and then select the unit of time for the filter. For example, to show only the three most recent weeks, select Weeks.

Here, you can clearly see that the default date is TODAY

Explanation

Data is generated and analyzed at many different levels of granularity. Granularity is the level of detail of the data. For example, when looking at graduation data, granularity would describe whether a row in the data set represents a single person or the graduating class of a university.

You should select Size on the Marks card and use the slider to adjust the size to uniformly change the size for all marks in a view. The Size property on the Marks card allows you to control the size of marks in the view by moving the slider to the left or right. The Size slider affects different marks in different ways, such as making them bigger or smaller, wider or narrower, or thicker or thinner1 The other options are not valid ways to uniformly change the size for all marks in a view. Selecting Label on the Marks card and then selecting Alignment will allow you to change the position of labels on marks, not the size of marks2 Using the Fit dropdown menu on the toolbar will allow you to change how the view fits within the worksheet, not the size of marks3 Selecting Format on the menu, and then selecting Cell Size will allow you to change the height and width of cells in a text table, not the size of marks in other types of views4

Discrete measures and discrete dimensions appear blue in Tableau. Discrete fields are those that have a finite number of distinct values, such as names, categories, or dates. Discrete fields are usually used to create headers or labels in the view. Blue fields indicate that the field is discrete. Continuous measures and continuous dimensions appear green in Tableau. Continuous fields are those that have an infinite range of possible values, such as numbers or ratios. Continuous fields are usually used to create axes or color gradients in the view. Green fields indicate that the field is continuous1

You can change the scope of a reference line by choosing one of the following options: Per Pane, Entire Table, or Per Cell. The scope determines how many reference lines are added to the view and how they are calculated. Per Pane adds one reference line for each pane in the view. Entire Table adds one reference line for the entire table in the view. Per Cell adds one reference line for each cell in the view

Explanation

When you connect to a new data source, Tableau assigns each field in the data source as dimension or measure in the Data pane, depending on the type of data the field contains. You use these fields to build views of your data.

Standard deviation is an aggregation that is available without requiring a table calculation or calculated field. Standard deviation is a statistical measure that shows how much variation there is from the average value in a set of data. Standard deviation is one of the predefined aggregations in Tableau that can be applied to any measure by selecting it from the context menu of the measure or from the drop-down menu on the Marks card6 The other options are not aggregations that are available without requiring a table calculation or calculated field. Running total, sample covariance, and percent of total are all examples of table calculations, which are computations that are applied to the values in an entire table or partition of a table. Table calculations can be created by selecting them from the context menu of a measure or by using functions in a calculated field7

Explanation

This is true, indeed!

The LEFT JOIN keyword returns all records from the left table (table1), and the matched records from the right table (table2). The result is NULL from the right side, if there is no match.

The Marks Card allows us not only to edit the Shape, Text and Colour, but also to modify the Tooltip and the level of detail of the visualisation!

The Marks card is a key element for visual analysis in Tableau. As you drag fields to different properties in the Marks card, you add context and detail to the marks in the view.

You use the Marks card to set the mark type (see Change the Type of Mark in the View), and to encode your data with color, size, shape, text, and detail. To change the mark settings, see Control the Appearance of Marks in the View.

In this example, three different fields have been dragged to different properties in the Marks card. Segment is on Color, Region is on Shape, and Quantity is on Size.

After you add a field to the Marks card, you can click the icon next to the field to change the property it is using. You can also click the property buttons in the Marks card to change those settings.

Many properties can have multiple fields. For example, you can add multiple fields to Label, Detail, Tooltip, and Color. Size and Shape can only have one field at a time. For more details, see Control the Appearance of Marks in the View.

Explanation

Here's how to add image files to your repository:

1) Find image file on the internet. I try to find consistent image formats if I plan to use a set of shapes such as logos or flags.

2) Download the image to your computer.

3) Drag images into your My Documents -> My Tableau repository -> Shapes folder.

4) Open Tableau and your new shapes will automatically be included in your "edit shapes" menu.

Explanation

Joins combine tables by adding more columns of data across similar row structures. This can cause data loss or duplication if tables are at different levels of detail, and joined data sources must be fixed before analysis can begin.

Inner join

Left Join

Blends, unlike relationships or joins, never truly combine the data. Instead, blends query each data source independently, the results are aggregated to the appropriate level, then the results are presented visually together in the view.

Two correct methods for creating a visual group are selecting marks in the view and right-clicking a dimension in the Data pane. A visual group is a way of combining related members in a dimension field to create categories or segments in your data. For example, you can create a visual group by selecting several states in a map view and grouping them into regions. You can create a visual group by selecting one or more marks in the view and then clicking the group icon on the tooltip or on the toolbar. This will create a new group field in the Data pane with default names for each group based on their members. You can also create a visual group by right-clicking a dimension in the Data pane and selecting Create > Group. This will open the Create Group dialog box where you can select several members and drag them into groups with custom names8 The other options are not correct methods for creating a visual group. Clicking the drop-down arrow from the top of the Data pane will open a menu with options for creating new fields, folders, sets, bins, etc., but not groups. Dragging a dimension onto another dimension in the Data pane will create a hierarchy, which is a way of organizing data into different levels of detail, not groups9

From the data pane, simply right click on the dimension, choose geographic role, and then select the appropriate role as follows:

Explanation

Follow along to reach the correct answer:

1) First, drag Date to the Column shelf and Profit to the Rows shelf. We need to see the 2 consecutive months over this two year period (2012-2013) so this tells us we need to work with continuous dates:

Click on Date in the Column shelf and convert it to continuous month :

2) Now, click on the Profit pill in the Rows shelf, go to quick table calculation and choose Percent difference:

3) Finally, click on the Show mark Labels icon:

4) We finally have our view, and clearly, 10 Months have a NEGATIVE percent difference:

Explanation

The option to create bins in Tableau is available for numeric fields. Bins allow you to group a series of numeric values into larger segments, which can simplify analysis and help in creating histograms or other visualizations that show the distribution of data. For example, you can create bins to group ages into categories like 0-10, 11-20, etc.

When users connect to Tableau, the data fields in their data set are automatically assigned a role and a type.

Role can be of the following two types:

1) Dimension

2) Measure

Type can be of the following :

1) String

2) Number

3) Geographic

4) Boolean

5) Date

6) Date and Time

Dates in Tableau will behave differently depending on whether they are a Datepart (blue) or a Datevalue (green). This affects how the axes display/behave and also how visualisations such as line charts will display. The difference essentially boils down to Dateparts behaving like a dimension as opposed to a measure which is how Datevalues behave. This means that Dateparts behave like discrete categories on the view whereas Datevalues are more like continuous numeric values.

Dateparts are discrete and they behave the same as dimension filters. If all dates are used on the filter then each individual date will be a datepart that can be selected/excluded. This is the same for each level of date, if datepart months is placed on filters January to December will be tick-able options in the filter. This also means that conditions and top/bottom filters can be applied to datepart filters like any other dimension filter.

Datevalues placed on filters behave like measure filters. A min and a max date can be set and there is a relative dates option which allows you to choose things like only show the previous 3 months or years etc.

In Tableau, when creating a combined axis chart, after dragging the first measure to the Rows shelf, you should drag the second measure directly onto the existing axis in the view. This will combine both measures on the same axis, allowing them to share a scale and an axis, which is the essence of a combined axis chart.

When we create a reference line, we get the following 3 options for the scope:

When exporting a view as an image in Tableau, the default export includes everything that is currently displayed in the view, with Tableau applying its own styling, including fonts. The default format for an exported image is PNG, which is a raster-based file format that captures all visual elements as pixels. PNG is widely used for its lossless compression and is suitable for detailed images like those produced by Tableau. Unlike vector-based images, PNGs do not embed fonts, and the image quality remains consistent when the image is viewed on different platforms, which makes it ideal for inserting into documents like Microsoft Word.

Explanation

We can very much blend multiple measures into a single axis. Such charts are called Combined-Axis / Blended-Axis charts. Follow along:

Measures can share a single axis so that all the marks are shown in a single pane. To blend multiple measures, drag one measure or axis and drop it onto an existing axis.

Instead of adding rows and columns to the view, when you blend measures there is a single row or column and all of the values for each measure is shown along one continuous axis. For example, the view below shows quarterly sales and profit on a shared axis.

Note: If you drag a measure on to the canvas and only see a single ruler indicator instead of the double ruler indicator shown below, Tableau creates dual axes instead of a blended axis. For more information about how to create dual axes, see Compare two measures using dual axes.

According to the official Tableau documentation:

Dimensions don't have aggregation properties, and adding properties manually each time defeats the whole DEFAULT aggregation purpose. Window tab doesn't have any default aggregation option!