Tableau TDS-C01 Tableau Desktop Specialist Exam Exam Practice Test

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.

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.

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

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

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

To visually group marks in Tableau, you typically start by selecting the marks directly in the view. Once the desired marks are selected, you can then create a group from them. This will visually group the selected marks, as indicated by the blue color in the example provided.

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.

Very important question for the exam and appears quite a lot too.

The correct answer is - By dragging another dimension to Color on the Marks card.

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This is what the question says we have already created. Now to convert this into a Stacked bar chart, we will drop another dimension on Color in the Marks card.

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The rest won't create stacked bar charts, and hence are incorrect choices. The best way to answer such questions on the real exam is to quickly do what the options say and see if they satisfy the requirements in the question.

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:

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.

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You can drag the filter by clicking on the 2 lines on top, and then dragging the filter as shown:

Explanation

Two use cases for creating hierarchies from the Data pane are to add drilldown functionality for fields and to organize related fields together. A hierarchy is a way of organizing data into different levels of detail. For example, a date hierarchy can have year, quarter, month, and day levels. A geographic hierarchy can have country, state, city, and zip code levels. By creating hierarchies from the Data pane, you can quickly drill down or up in a hierarchy to add or subtract more levels of detail in the view. You can also use hierarchies to group related fields together in the Data pane, making it easier to find and use them14 The other options are not valid use cases for creating hierarchies from the Data pane. To concatenate all fields into a single field, you need to use a calculated field or a join, not a hierarchy. To create faster-performing queries, you need to optimize your data source, filters, calculations, or extracts, not create hierarchies

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/

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.

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

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.

Before you can build a view and analyze your data, you must first connect Tableau to your data. Tableau supports connecting to a wide variety of data, stored in a variety of places.

By default, when you connect a data source to Tableau, Tableau will create a live connection to the data.

Live connection refers to a data source that contains direct connection to underlying data, which provides real-time or near real-time data. With a live connection, Tableau makes queries directly against the database or other source, and returns the results of the query for use in a workbook. Users can create live connections and then share them on Tableau Server so that other Tableau users can use the same data using the same connection and filtering settings.

Explanation

It is possible to open a Tableau support case by visiting the following link : https://www.tableau.com/support/case

Explanation

Yes! It is possible to duplicate a field and add it to more than one hierarchy. Right click and choose duplicate.

According to the official Tableau documentation:

An example of an area chart is shown below:

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.

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

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.

*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

Whenever we want to add actions to a Dashboard, we can trigger then in the following 3 ways:

1) Select

2) Hover

3) Menu

Hover is best for highlighting, select for filtering. Menu action is added to the tooltip and user can decide whether to run that action or not (best for URL actions)

Yes! We get different options for filtering depending on whether we use a categorical data (think dimension) or quantitative data (think measure).

Important Note: Each tab adds additional definitions to your filter. For example, you can select to exclude values under the General tab, and also add limits under the Top tab. Selections and configurations from both tabs are applied to your filter.At any time, you can see the definitions of your filter under Summary on the General tab.

Note: If you have a large data source, filtering measures can lead to a significant degradation in performance. It is sometimes much more efficient to filter by creating a set containing the measure and then apply a filter to the set. For more information about creating sets, see Create Sets

Padding lets you precisely space items on dashboard, while borders and background colors let you visually highlight them. Inner padding sets the spacing between item contents and the perimeter of the border and background color; outer padding provides additional spacing beyond the border and background color.

From the official Tableau documentation:

As we can see below, Bar charts can be used as a floating object.

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:

To update the font of an entire workbook in Tableau, you should use the "Format Workbook" pane. This feature allows you to set and modify the default font settings for the entire workbook, ensuring consistency in font style across all sheets and dashboards. It's a global setting that applies to all visual elements in the workbook, including titles, captions, axis labels, and other text elements.

Explanation

For relational data sources, dates and times are automatically placed in the Dimensions area of the Data pane and are identified by the date or date-time icon. For example, the Order Date and Ship Date dimensions from an Excel data source are shown below:

When you place a relational date on a shelf, the field name is automatically modified to reflect the default date level. Tableau defines the default date level to be the level at which there are multiple instances. For example, if the date field includes multiple years, the default level is year. However, if the date field contains data for just one year but includes multiple months, then the default level is month.

To provide additional information when hovering over a field in the Data pane, as shown in the exhibit, you need to configure a default comment for the field. This comment can be set by right-clicking on the field in the Data pane and selecting "Default Properties" and then "Comment." The text entered as a comment here will then be displayed as a tooltip when hovering over the field in the Data pane.

According to Tableau's official documentation:

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This is one of the most common questions on the Tableau Desktop Specialist Exam.

Wrong options -

1) Don't use backgrounds - This is not a solution. What if we want to use backgrounds? We can't just stop using backgrounds to solve this problem.

2) Decrease the font size - Do you think using a smaller font will make the text more readable? No right? Hence, this is wrong too.

All other options are ways recommended to make your text more readable!

Explanation

This question is directly from the Official Tableau Desktop Specialist exam guide.

Since we are talking about the AVERAGE home values for the United States in 2016, the question is directly offering us a hint that the answer has something to do with aggregation and that too the values tell us that we're working with MEASURES.

Date part and Date values don't really make much sense given the question, and neither does geography.

Therefore, the answer naturally is "An aggregated measure".

Explanation

Follow the steps to get the correct answer : January 2018

• We are talking about dates, so use the Time series sheet as follows:
• Next, the following should be your view and clearly, January 2018 is the lowest point:

Read more about dates: https://interworks.com/blog/rcurtis/2017/01/30/tableau-deep-dive-dates-introduction-dates/

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.

According to the official documentation, the following are the requirements for using relationships:

1) When relating tables, the fields that define the relationships must have the same data type. Changing the data type in the Data Source page does not change this requirement. Tableau will still use the data type in the underlying database for queries.

2) You can't define relationships based on geographic fields.

3) Circular relationships aren't supported in the data model.

4) You can't edit relationships in a published data source.

5) You can't define relationships between published data sources.

6) Your workbook must use an embedded data source for you to be able to edit relationships and performance options in the Data Source page in Tableau Online or Tableau Server.

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

Cluster analysis partitions marks in the view into clusters, where the marks within each cluster are more similar to one another than they are to marks in other clusters.

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

Explanation

Yes, it is very much possible to use measures in the same view multiple times. For example, refer to the image below:

We are using BOTH the Sum of the revenue and the AVG of the revenue in the same view!

Dragging a continuous date value to the Rows shelf in Tableau results in a green pill on the Rows shelf, indicating a continuous field. It also results in a quantitative axis being displayed on the visualization. Continuous fields are used to create axes on charts and can represent a range of values smoothly. This is opposed to discrete date values, which would be represented by a blue pill and typically show headers or labels rather than a continuous axis.

Unioning is the correct answer!

From the official documentation:

For example, suppose you have the following customer purchase information stored in three tables, separated by month. The table names are "May2016" , "June2016" and "July2016."

The only incorrect option is - Click on Dashboard in the Menu bar, select Tooltip, and then use the italics option.

This option doesn't exist. See below:

The rest of the options do exist, and therefore are correct:

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Explanation

The different aggregations available for a measure determine how the individual values are collected: they can be added (SUM), averaged (AVG), or set to the maximum (MAX) or minimum (MIN) value from the individual row values.

For a complete list of the available aggregations, check out - List of Predefined Aggregations in Tableau.

The level of detail is determined by the dimensions in your view—for information about the concept of level of detail, see How dimensions affect the level of detail in the view.

Disaggregating your data can be useful for analyzing measures that you may want to use both independently and dependently in the view. For example, you may be analyzing the results from a product satisfaction survey with the Age of participants along one axis. You can aggregate the Age field to determine the average age of participants or disaggregate the data to determine at what age participants were most satisfied with the product.

Disaggregating data can be useful when you are viewing data as a scatter plot. See Example: Scatter Plots, Aggregation, and Granularity.

Explanation

To reach the correct answer, follow the steps below:

1) Drag Category, and sub-category to the row shelf. Drag Profit to the Column shelf

2) Click the Sort-ascending icon as shown above, to sort the profits from least to greatest as shown:

Click the 'Show mark labels icon'

As we can see, Fasteners has the least Profit in the Office Supplies Category, and hence is our correct answer!

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 dealing with a union of two tables in Tableau, using logical tables with a single table extract is the most appropriate approach. Logical tables allow for the integration of data from multiple tables in a way that is seamless and efficient for analysis. By using a single table extract, Tableau consolidates the data from the union into one extract, optimizing performance and enabling faster data processing. This approach is particularly beneficial when working with large datasets or complex unions.

In Tableau, to create a reference line, one of the axes in the view must contain an aggregated measure. Reference lines are typically used to add context or additional information to the data in a view, and they are often based on some form of aggregate such as the average, median, sum, etc., of a measure.

Explanation

After making changes to Data, we can save that new data source as a .tds file. To do so, go to data menu on top and then choose your current connected data source. Then next click on Add to Saved Data sources. This will save all calculated fields, changes to fields etc. It will be saved in My Tableau Repository -> Mydatasources. This will then also appear on Tableau Home Page under saved data sources like SampleSuperStore.

Note: Data source files do not contain the actual data but rather the information necessary to connect to the actual data as well as any modifications you've made on top of the actual data such as changing default properties, creating calculated fields, adding groups, and so on.

twb and .twbx are not the BEST solutions since the questions nowhere mentions that we need to store our workbooks as well.

twm is a bookmark which contains a single worksheet and are an easy way to quickly share your work.

All are correct, except - Copy of any local file-based data. This is contained in a .tdsx file (Tableau Packaged Data Source)!

According to the official documentation -

Explanation

VERY IMPORTANT QUESTION FOR THE EXAM, PAY ATTENTION

1) To find the number of days between order date and shipping date, we will make use of a calculated field:

In the data pane, click on the dropdown arrow, and choose create calculated field.

Let's name this calculated field "ShippingDelay" (you can name it anything you want :) )

2) Use the DATEDIFF() function, and pass it the arguments as follows:

'day' depicts that we want to calculate the number of DAYS between the two dates. The first argument is 'start_date' which is the ORDER_DATE (day the order was placed), the second argument is 'end_date', which is the SHIP_DATE (date the order was shipped). So by subtracting as follows: SHIP_DATE - ORDER_DATE, we can find the delay in shipping.

Click OK.

3) You should now have a new measure as follows:

4) Phew! The hard part is done! Now let's filter by Market to include only LATAM:

5) Drag Country to the view, and the new calculated field 'ShippingDelay' to SIZE on the Marks Shelf as follows:

*You can also click on Show Text Labels to be sure that you're choosing the Largest value*

Clearly, Mexico has the highest Shipping Delay!

Explanation

Tricky options! Go to hyperlink and Go to Web page are not valid Actions in Tableau.

A URL action is a hyperlink that points to a web page, file, or other web-based resource outside of Tableau. You can use URL actions to create an email or link to additional information about your data. To customize links based on your data, you can automatically enter field values as parameters in URLs.

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From the official documentation:

When you have a view with a large amount of data you might want to explore your data interactively and highlight a specific mark or group of marks while still maintaining the context of where those marks show in your view.

To do this you can turn on the Highlighter for one or more discrete fields that are included in your view and that affect the level of detail

Example - Here we just want to focus on Public Affairs college dimension, but don't want to filter out or remove the rest of the data:

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Note that filtering is not the correct option since that would REMOVE the data that doesn't match the filtering criteria.

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

Explanation

Tableau SERVER enables us to create workbooks and views, dashboards, and data sources in Tableau Desktop, and then publish this content to our own server.

Moreover, as a Tableau Server administrator you will control who has access to server content to help protect sensitive data. Administrators can set user permissions on projects, workbooks, views, and data sources.

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.

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

Beginning in version 10.5, when you create a new extract, it uses the .hyper format instead of the .tde format.

Extracts in the .hyper format take advantage of the improved data engine, which supports the same fast analytical and query performance as the data engine before it, but for even larger extracts.

Although there are many benefits of using .hyper extracts, the primary benefits include the following:

1) Create larger extracts: You can create extracts with billions of rows of data. Because .hyper extracts can support more data, you can consolidate .tde extracts that you previously had to create separately into a single .hyper extract.

2) Create and refresh extracts faster: While Tableau has always optimized performance for creating and refreshing extracts, version 2020.3 supports faster extract creation and refreshes for even larger data sets.

3) Experience better performance when interacting with views that use extract data sources: Although smaller extracts continue to perform efficiently, larger extracts perform more efficiently.

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.

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.

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

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

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Explanation

The word 'static tool' gives it away.

According to the official website :

Measure names and values CANNOT be deleted in Tableau like other columns can. These are auto-generated.

Calculated Fields, and Number of records can both be deleted.

Upon clicking on a filter, we see the following options:

Clearly, Regex Match is not one of these options!

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:

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

Explanation

SUM is an aggregate function, not a table calculation!

A table calculation is a transformation you apply to the values in a visualization. Table calculations are a special type of calculated field that computes on the local data in Tableau. They are calculated based on what is currently in the visualization and do not consider any measures or dimensions that are filtered out of the visualization.

The most common Table calculations are:

Running Total

Percent Difference

Difference

Percent of Total

Rank

Percentile

These can be calculated using : Table(across), Cell, or Specific dimensions!

Explanation

Tableau auto-generates :

1 Dimension - Measure Names

4 Measures - Latitude, Longitude, Number of records, Measure Values

Starting with Tableau 2020.2, every table in a data source has a Count field, in the form of NameofTable(Count). The table count field is an automatically generated, calculated field. (THIS IS NOT PRESENT IN VERSION 2020.1 ON WHICH THE EXAM IS CURRENTLY BASED)

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:

Important question!

Three statements that accurately describe dimensions are: Contain qualitative values Affect the level of detail in the view Can be continuous or discrete Dimensions are 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 by creating headers or labels for marks or by defining how data is partitioned for table calculations. Dimensions can be continuous or discrete depending on their data type and format. Continuous dimensions are those that have an infinite range of possible values, such as numbers or ratios. Discrete dimensions are those that have a finite number of distinct values, such as names, categories, or dates3 The other options are not accurate statements about dimensions. Contain numeric, quantitative values is not correct, because fields that contain numeric, quantitative values are measures, not dimensions. Measures are fields that contain numeric values that you can measure and aggregate. Affect the formatting options in the view is not correct, because formatting options are not determined by dimensions, but rather by user preferences and choices. Formatting options allow you to change the appearance of various elements in the workbook, such as fonts, borders, shading, alignment, etc3

The benefits of using a live connection over an extract include:

• B: A live connection ensures that the data in the workbook is as up-to-date as the database itself, providing fresher data compared to a static extract which is updated at intervals.
• C: A live connection queries the database server directly, which means it uses the server's memory and processing power rather than relying on the client computer's resources.

A live connection does not necessarily require less network overhead (A) as it may continually send queries over the network, and it is not always faster than an extract (D) because extracts can provide quicker response times for complex queries or large datasets.

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'

From the official documentation, the following are the major advantages of using Tableau Data Extracts:

From Tableau 2020.2 onwards, .hyper is the recommended way since it is faster than .tde!

Presently, there is no official way to connect your data in Firebase directly with Tableau Desktop.

A workaround however can be to export your Firebase data into Google BigQuery, and then connect it to Tableau Desktop.

But then again, it is a workaround and not an official out-of-the-box solution.

The following are the available Data sources available as of now:

1) Server

2) File

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:

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

According to the official Tableau documentation:

Depending on the version the extract was created in, Tableau extract files can have either the .hyper or .tde file extension. Extract files are a local copy of a subset or entire data set that you can use to share data with others, when you need to work offline, and improve performance. For more information, see Extract Your Data.

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.

The basic building blocks for a histogram are as follows:

Demo :

 You can share a dashboard as an image file by using one of the following methods: Export Image on the Dashboard menu, or a dashboard export button. Export Image on the Dashboard menu allows you to export the dashboard as an image file in BMP, JPEG, PNG, or SVG format. A dashboard export button is a custom button that you can create on your dashboard to export it as an image file using a URL action

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.

From the official Tableau documentation:

You can union your data to combine two or more tables by appending values (ROWS) from one table to another. To union your data in Tableau data source, the tables must come from the same connection.

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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.

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By default, the shape that a Heap map uses is a "Square". See below:

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

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.

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.

Further,

In Tableau, the two elements that can have their values changed by using a dashboard action are Sets and Parameters. Dashboard actions can be configured to modify the values within a set or a parameter, allowing for interactive and dynamic changes in the visualization based on user interactions. For example, selecting a specific data point in a dashboard can trigger an action that updates a set or changes the value of a parameter, which in turn can alter the displayed data or the appearance of visualizations within the dashboard.

Explanation

By default, measures placed in a view are aggregated by SUM, which means that the data for that field in all of the rows is combined. Measures can also be aggregated as average, median, count, or count distinct.

Exporting the visualisation gives us a static view of the visualisation.

It is NOT possible to interact with it, use filters, or dynamically enter anything in a visualisation exported PDF.

In Tableau Desktop, you can save views as PDF files rather than printing them as hard copies. You do not need to have Adobe Acrobat installed on your computer.

When you print an individual sheet to PDF, filters in the view are not included. To show filters, create a dashboard containing the sheet and export the dashboard to PDF. (IMPORTANT)

No, by default, animations are not enabled in Tableau.

We can animate visualizations to better highlight changing patterns in your data, reveal spikes and outliers, and see how data points cluster and separate.

Animations visually transition between filter, sort, and zoom settings, different pages, and changes to filter, parameter, and set actions. As visualizations animate in response to these changes, viewers can more clearly see how data differs, helping them make better informed decisions.

When you author animations, you can choose between two different styles: simultaneous or sequential. Here are examples of each type.

1) Simultaneous animations

The default simultaneous animations are faster and work well when showing value changes in simpler charts and dashboards.

2) Sequential animations

Sequential animations take more time but make complex changes clearer by presenting them step-by-step.

To Animate visualizations in a workbook:

1) Choose Format > Animations.

2) If you want to animate every sheet, under Workbook Default, click On. Then do the following:

For Duration, choose a preset, or specify a custom duration of up to 10 seconds.

For Style, choose Simultaneous to play all animations at once or Sequential to fade out marks, move and sort them, and then fade them in.

3) To override workbook defaults for a particular sheet, change the settings under Selected Sheet.

According to the official documentation:

Joins are a more static way to combine data. Joins must be defined between physical tables up front, before analysis, and can’t be changed without impacting all sheets using that data source. Joined tables are always merged into a single table. As a result, sometimes joined data is missing unmatched values, or duplicates aggregated values.

Joins -

1) Are displayed with Venn diagram icons between physical tables

2) Require you to select join types and join clauses

3) Joined physical tables are merged into a single logical table with a fixed combination of data

4) May drop unmatched measure values

5) May duplicate aggregate values when fields are at different levels of detail

6) Support scenarios that require a single table of data, such as extract filters and aggregation