Desktop-Specialist Exam Dumps - Tableau Desktop Specialist Questions and Answers

According to the Tableau Help, a highlight table is “a cross-tabulation that uses color to encode values”. The help also states that “Highlight tables use the Square mark type” (page 1).

Explanation

You can show trend lines in a visualization to highlight trends in your data.

To add trend lines to a view, both axes must contain a field that can be interpreted as a number. For example, you cannot add a trend line to a view that has the Product Category dimension, which contains strings, on the Columns shelf and the Profit measure on the Rows shelf.

However, you can add a trend line to a view of sales over time because both sales and time can be interpreted as numeric values.

Explanation

The following are 2 correct ways to copy the worksheet visualisation as an image:

AND

Using the Sample superstore as a reference, click on the manage metadata icon as follows:

We can rename a particular column name to make it easier to remember and use in Tableau. Let's change Order ID to oID as shown:

Now, we'll see oID when using this data source in Tableau. This WILL NOT affect the original data source. The remote field name let's us see what the name of the column is in the ORIGINAL Data source.

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!

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.

Graphical user interface, application Description automatically generated

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.

According to the official Tableau documentation:

An example of an area chart is shown below:

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

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!